U.S. patent application number 14/382076 was filed with the patent office on 2015-01-22 for method and system for real time dry low nitrogen oxide (dln) and diffusion combustion monitoring.
The applicant listed for this patent is Nuovo Pignone Srl. Invention is credited to Osama Naim Ashour, Alessandro Betti, David Bianucci, Riccardo Crociani, Nicola Giannini, Abdurrahman Abdallah Khalidi, Antonio Pumo, Arul Saravanapriyan.
Application Number | 20150025814 14/382076 |
Document ID | / |
Family ID | 46051732 |
Filed Date | 2015-01-22 |
United States Patent
Application |
20150025814 |
Kind Code |
A1 |
Giannini; Nicola ; et
al. |
January 22, 2015 |
METHOD AND SYSTEM FOR REAL TIME DRY LOW NITROGEN OXIDE (DLN) AND
DIFFUSION COMBUSTION MONITORING
Abstract
A system and method for monitoring and diagnosing anomalies in a
diffusion or dry low NO.sub.X combustion system of a gas turbine,
the method including storing a plurality rule sets specific to a
temperature spread of the gas turbine exhaust. The method further
including determining an anomaly in the performance of the gas
turbine using at least one of a swirl angle of the exhaust flow, a
health of a plurality of flame detectors of the gas turbine, and a
transfer of the gas turbine from a first mode of operation to a
second lower NO.sub.X mode of operation, and recommending to an
operator of the gas turbine a set of corrective actions to correct
the anomaly.
Inventors: |
Giannini; Nicola; (Firenze,
IT) ; Khalidi; Abdurrahman Abdallah; (Doha, QA)
; Saravanapriyan; Arul; (Doha, QA) ; Bianucci;
David; (Firenze, IT) ; Pumo; Antonio;
(Firenze, IT) ; Betti; Alessandro; (Firenze,
IT) ; Crociani; Riccardo; (Firenze, IT) ;
Ashour; Osama Naim; (Doha, QA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nuovo Pignone Srl |
Florence |
|
IT |
|
|
Family ID: |
46051732 |
Appl. No.: |
14/382076 |
Filed: |
March 1, 2013 |
PCT Filed: |
March 1, 2013 |
PCT NO: |
PCT/EP2013/054156 |
371 Date: |
August 29, 2014 |
Current U.S.
Class: |
702/35 |
Current CPC
Class: |
F04B 51/00 20130101;
G05B 11/06 20130101; G05B 23/0218 20130101; G05B 23/0245 20130101;
F01D 21/003 20130101; G05B 23/0216 20130101; G01M 15/14 20130101;
F05D 2260/80 20130101; G05B 23/0283 20130101; G01K 13/00 20130101;
F02C 7/00 20130101; H04L 67/10 20130101; G01L 3/10 20130101 |
Class at
Publication: |
702/35 |
International
Class: |
G01M 15/14 20060101
G01M015/14 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 1, 2012 |
IT |
CO2012A000008 |
Claims
1. A computer-implemented method for monitoring and diagnosing
combustion anomalies in an operation of a gas turbine, the method
implemented using a computer device coupled to a user interface and
a memory device, the method comprising: storing a plurality rule
sets in a memory device, the rule sets relative to the operation of
the gas turbine, the rule sets comprising at least one rule
expressed as a relational expression of a real-time data output
relative to a real-time data input, the relational expression being
specific to at least one of a temperature spread of an exhaust flow
of the gas turbine, a swirl angle of the exhaust flow, a health of
a plurality of secondary flame detectors of the gas turbine, and a
transfer of the gas turbine from a first mode of operation to a
second lower NO.sub.X mode of operation; receiving real-time and
historical data inputs from a condition monitoring system
associated with the gas turbine, the data inputs relating to
parameters affecting at least one of the temperature spread of the
exhaust flow of the gas turbine, the swirl angle of the exhaust
flow, the health of the plurality of flame detectors of the gas
turbine, and the transfer of the gas turbine from the first mode of
operation to the second lower NO.sub.X mode of operation;
determining a fuel gas line pressure drop using the received data;
comparing the determined pressure drop to a predetermined threshold
range; and recommending to an operator of the gas turbine to
transfer the mode of operation of the gas turbine from the first
mode to the second mode without reducing a load of the gas turbine
if the determined pressure drop meets the predetermined threshold
range.
2. The method of claim 1, wherein storing a plurality rule sets
comprises storing a gas turbine transfer rule set wherein the first
mode of operation is an Extended Lean-Lean (EXT-LL) mode and the
second lower NO.sub.X mode of operation is a Premix mode.
3. The method of claim 1, further comprising: receiving an analog
signal output of at least some of the plurality of flame detectors;
statistically analyzing each analog signal output to identify a
noise component of the signal and a variation of the signal;
generating a health count metric of the signals to define a
plurality of thresholds based on the analysis; comparing a current
analog signal output to respective threshold; and outputting a
recommendation to at least one of replace one of the plurality of
flame detectors, tune one of the plurality of flame detectors,
check the operation of one of the plurality of flame detectors, and
clean a lens of one of the plurality of flame detectors.
4. The method of claim 1, further comprising: determining a swirl
angle of a flow of gas turbine exhaust; determining a faulty
combustor using the determined swirl angle; and outputting the
determined faulty combustor to an operator.
5. The method of claim 4, wherein determining a swirl angle
comprises: receiving a plurality of temperature outputs from one or
more temperature sensors associated with the flow of gas turbine
exhaust; and determining a temperature spread of the flow of gas
turbine exhaust using the received plurality of temperature
outputs.
6. The method of claim 5, further comprising correlating the
determined temperature spread to a predetermined allowable
temperature spread to determine an identity of a source combustor
of the temperature spread.
7. The method of claim 5, wherein determining a temperature spread
of the flow of gas turbine exhaust comprises determining a
temperature spread of the flow of gas turbine exhaust at an exhaust
diffuser of the gas turbine.
8. The method of claim 5, wherein determining a temperature spread
of the flow of gas turbine exhaust comprises determining a
temperature spread of the flow of gas turbine exhaust as a function
of combustion mode and load.
9. The method of claim 5, wherein the gas turbine is capable of
operating in a plurality of different combustion modes, the method
further comprising determining a temperature spread threshold for
each different combustion mode.
10. The method of claim 9, further comprising setting a temperature
spread threshold to a value corresponding to a combustion mode
being entered at least one of coincident to the transition into the
combustion mode being entered and prior to the transition into the
combustion mode being entered.
11. A system for monitoring and diagnosing combustion anomalies in
an operation of a gas turbine, the system comprising: a memory
device; a condition monitoring system associated with the gas
turbine; an user interface; and a process configured to: store a
plurality rule sets in the memory device, the rule sets relative to
the operation of the gas turbine, the rule sets comprising at least
one rule expressed as a relational expression of a real-time data
output relative to a real-time data input, the relational
expression being specific to at least one of a temperature spread
of an exhaust flow of the gas turbine, a swirl angle of the exhaust
flow, a health of a plurality of secondary flame detectors of the
gas turbine, and a transfer of the gas turbine from a first mode of
operation to a second lower NO.sub.X mode of operation, receive
real-time and historical data inputs from the condition monitoring
system, the data inputs relating to parameters affecting at least
one of the temperature spread of the exhaust flow of the gas
turbine, the swirl angle of the exhaust flow, the health of the
plurality of flame detectors of the gas turbine, and the transfer
of the gas turbine from the first mode of operation to the second
lower NO.sub.X mode of operation, determine a fuel gas line
pressure drop using the received data, compare the determined
pressure drop to a predetermined threshold range, and recommend
through the user interface to an operator of the gas turbine to
transfer the mode of operation of the gas turbine from the first
mode to the second mode without reducing a load of the gas turbine
if the determined pressure drop meets the predetermined threshold
range.
12. The system of claim 11, wherein storing a plurality rule sets
comprises storing a gas turbine transfer rule set wherein the first
mode of operation is an Extended Lean-Lean (EXT-LL) mode and the
second lower NO.sub.X mode of operation is a Premix mode.
13. The system of claim 11, wherein the processor is further
configured to: receive an analog signal output of at least some of
the plurality of flame detectors, statistically analyze each analog
signal output to identify a noise component of the signal and a
variation of the signal, generate a health count metric of the
signals to define a plurality of thresholds based on the analysis,
compare a current analog signal output to respective threshold, and
output a recommendation to at least one of replace one of the
plurality of flame detectors, tune one of the plurality of flame
detectors, check the operation of one of the plurality of flame
detectors, and clean a lens of one of the plurality of flame
detectors.
14. The system of claim 11, wherein the processor is further
configured to: determine a swirl angle of a flow of gas turbine
exhaust, determine a faulty combustor using the determined swirl
angle, and output the determined faulty combustor to an
operator.
15. The system of claim 14, wherein determining a swirl angle
comprises: receiving a plurality of temperature outputs from one or
more temperature sensors associated with the flow of gas turbine
exhaust; and determining a temperature spread of the flow of gas
turbine exhaust using the received plurality of temperature
outputs.
16. The system of claim 15, wherein the processor is further
configured to correlate the determined temperature spread to a
predetermined allowable temperature spread to determine an identity
of a source combustor of the temperature spread.
17. The system of claim 15, wherein determining a temperature
spread of the flow of gas turbine exhaust comprises determining a
temperature spread of the flow of gas turbine exhaust at an exhaust
diffuser of the gas turbine.
18. The system of claim 15, wherein determining a temperature
spread of the flow of gas turbine exhaust comprises determining a
temperature spread of the flow of gas turbine exhaust as a function
of combustion mode and load.
19. The system of claim 15, wherein the gas turbine is capable of
operating in a plurality of different combustion modes, and the
processor is further configured to determine a temperature spread
threshold for each different combustion mode.
20. The system of claim 19, wherein the processor is further
configured to set a temperature spread threshold to a value
corresponding to a combustion mode being entered at least one of
coincident to the transition into the combustion mode being entered
and prior to the transition into the combustion mode being entered.
Description
FIELD OF THE INVENTION
[0001] This description relates to generally to
mechanical/electrical equipment operations, monitoring and
diagnostics, and more specifically, to systems and methods for
automatically advising operators of anomalous behavior of
machinery.
BACKGROUND OF THE INVENTION
[0002] The combustion system is an important item to be monitored
in a gas turbine. Traditional combustion monitoring systems use
static thresholds that do not consider the machine operating
conditions, such as combustion mode and load. As a result, they are
inefficient and produce false or too-late alarms. For example, many
hours are currently spent locating a source of a fault in the case
of a real exhaust temperature spread issue. On the flame detector
side, monitoring the digital signal only or analog output without a
correct statistical approach is problematic and results in false
warning.
[0003] Traditional monitoring systems suffer from technical
deficiencies. Inaccuracy is the most evident, as seen by either too
many false alarms or too late alarms are generally reported,
without taking into account machine operating conditions; thus, no
troubleshooting or little information is provided.
SUMMARY OF THE INVENTION
[0004] In one embodiment, a computer-implemented method for
monitoring and diagnosing anomalies in an operation of a gas
turbine, the method implemented using a computer device coupled to
a user interface and a memory device, the method comprising storing
a plurality rule sets in the memory device, the rule sets relative
to the operation of the gas turbine, the rule sets including at
least one rule expressed as a relational expression of a real-time
data output relative to a real-time data input, the relational
expression being specific to at least one of a temperature spread
of an exhaust flow of the gas turbine, a swirl angle of the exhaust
flow, a health of a plurality of flame detectors of the gas
turbine, and a transfer of the gas turbine from a first mode of
operation to a second lower NO.sub.X mode of operation, receiving
real-time and historical data inputs from a condition monitoring
system associated with the gas turbine, the data inputs relating to
parameters affecting at least one of the temperature spread of the
exhaust flow of the gas turbine, the swirl angle of the exhaust
flow, the health of the plurality of flame detectors of the gas
turbine, and the transfer of the gas turbine from the first mode of
operation to the second lower NO.sub.X mode of operation,
determining a fuel gas line pressure drop using the received data,
comparing the determined pressure drop to a predetermined threshold
range, and recommending to an operator of the gas turbine to
transfer the mode of operation of the gas turbine from the first
mode to the second mode without reducing a load of the gas turbine
if the determined pressure drop meets the predetermined threshold
range.
[0005] In another embodiment, a gas turbine monitoring and
diagnostic system for a gas turbine includes an axial compressor
and a low pressure turbine in flow communication, said system
comprising a real-time DLN and diffusion combustion rule set, the
rule set including a relational expression of a real-time data
output relative to at least one of the temperature spread of the
exhaust flow of the gas turbine, the swirl angle of the exhaust
flow, the health of the plurality of flame detectors of the gas
turbine, and the transfer of the gas turbine from the first mode of
operation to the second lower NO.sub.X mode of operation.
[0006] In yet another embodiment, one or more non-transitory
computer-readable storage media has computer-executable
instructions embodied thereon, wherein when executed by at least
one processor, the computer-executable instructions cause the
processor to store a plurality rule sets in the memory device, the
rule sets relative to the output of the gas turbine, the rule sets
including at least one rule expressed as a relational expression of
a real-time data output relative to a real-time data input, the
relational expression being specific to at least one of a
temperature spread of an exhaust flow of the gas turbine, a swirl
angle of the exhaust flow, a health of a plurality of flame
detectors of the gas turbine, and a transfer of the gas turbine
from a first mode of operation to a second lower NO.sub.X mode of
operation, receive real-time and historical data inputs from a
condition monitoring system associated with the gas turbine, the
data inputs relating to parameters affecting at least one of the
temperature spread of the exhaust flow of the gas turbine, the
swirl angle of the exhaust flow, the health of the plurality of
flame detectors of the gas turbine, and the transfer of the gas
turbine from the first mode of operation to the second lower
NO.sub.X mode of operation, receive a plurality of temperature
outputs from one or more temperature sensors associated with the
flow of gas turbine exhaust, and determine a temperature spread of
the flow of gas turbine exhaust using the received plurality of
temperature outputs.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIGS. 1-10 show exemplary embodiments of the method and
system described herein.
[0008] FIG. 1 is a schematic block diagram of a remote monitoring
and diagnostic system in accordance with an exemplary embodiment of
the present invention.
[0009] FIG. 2 is a block diagram of an exemplary embodiment of a
network architecture of a local industrial plant monitoring and
diagnostic system, such as a distributed control system (DCS).
[0010] FIG. 3 is a block diagram of an exemplary rule set that may
be used with LMDS shown in FIG. 1.
[0011] FIG. 4 is a side elevation view of a gas turbine engine in
accordance with an exemplary embodiment of the present
disclosure.
[0012] FIG. 5 is a schematic representation of the placement of
twelve thermocouples spaced approximately evenly about diffuser in
accordance with an exemplary embodiment of the present
disclosure.
[0013] FIG. 6 is a graph illustrating a correlation between burner
clogging and exhaust temperature spread.
[0014] FIG. 7 is a schematic block diagram of a flame detector (FD)
circuit that may be used with gas turbine engine shown in FIG. 4 in
accordance with an exemplary embodiment of the present
disclosure.
[0015] FIG. 8 is a screen capture of a trace of flame detector
circuit analog outputs and digital output.
[0016] FIG. 9 is a flow diagram of operation of gas turbine engine
during a loading and an unloading process.
[0017] FIG. 10 is a schematic piping diagram of a portion of a fuel
system 1000 that may be used with gas turbine engine shown in FIG.
4 in accordance with an exemplary embodiment of the present
disclosure.
[0018] Although specific features of various embodiments may be
shown in some drawings and not in others, this is for convenience
only. Any feature of any drawing may be referenced and/or claimed
in combination with any feature of any other drawing.
DETAILED DESCRIPTION OF THE INVENTION
[0019] The following detailed description illustrates embodiments
of the invention by way of example and not by way of limitation. It
is contemplated that the invention has general application to
analytical and methodical embodiments of monitoring equipment
operation in industrial, commercial, and residential
applications.
[0020] The combustion system is an important item to be monitored
in a gas turbine. Dry Low NOx (DLN) systems are more complicated
and involve different combustion modes than traditional gas
turbines. As used herein, NOx refers to mono-nitrogen oxides, NO
and NO2 (nitric oxide and nitrogen dioxide). The real-time DLN and
diffusion combustion rule set facilitates preventing incorrect
combustion operation, identifying direct guidelines for
troubleshooting, and warns against early signs of failure, giving
gas turbine operators time to act and/or schedule shut downs.
[0021] The real-time DLN and diffusion combustion rule set includes
the following combustion rules as part of an online monitoring
system:
[0022] 1. Exhaust temperature spread as a function of combustion
mode and load: For a DLN system, specifying one constant threshold
for the exhaust temperature spread will lead to either false alarms
or too late alarms. There is a transition sequence during which the
combustion transfers from one mode to another; for example:
Primary, Lean-Lean, Secondary, Premix or Extended Lean-Lean. During
each mode, a proper threshold for the spread is identified and when
loading the machine in Lean-Lean mode, the spread is specified as a
function of the firing temperature. For diffusion combustion the
spread is compared with a threshold which is, for example,
approximately 0.7 the allowable spread. A rule to highlight exhaust
temperature thermocouple sensor failure is also defined.
[0023] Exhaust spread thresholds are set more accurately, because
they are set for each combustion mode and as a function of load.
The exhaust spread alarms are validated to ensure a real issue is
the cause. The real-time DLN and diffusion combustion rule set
facilitates and enhances troubleshooting: for example, the swirl
angle calculator locates the source of fault (combustor(s) and/or
fuel nozzles) and reduces troubleshooting time. On diffusion
combustion gas turbine, a rule is also provided that determines
whether a high spread is caused by a faulty sensor, so that the
troubleshooting process can immediately proceed towards the right
root cause identification.
[0024] 2. The real-time DLN and diffusion combustion rule set also
performs a swirl angle calculation to trace back the spread to a
source or a faulty combustor(s), which will significantly reduce
troubleshooting time. When a spread is detected on a multi-can gas
turbine, it is not straightforward to judge on the source of the
problem (faulty combustor), because the thermocouples are not
placed adjacent to the combustor cans. The rule set traces back
from the spread anomaly at the exhaust diffuser to the faulty
combustor. A correlation used in the rule engine identifies the
faulty combustor in the event of a real spread.
[0025] 3. Flame detector health is important too, as flame detector
degradation over time and other problems can lead to multiple trips
with all associated costs and loss of production. The real time DLN
and diffusion combustion rule set includes an algorithm that
analyzes the health of flame detectors and generates warnings and
recommendations related to this real time analysis, which
facilitates performance of good maintenance of the flame detector
system to avoid false loss-of-flame alarms and trips. The raw pulse
signal coming from the flame sensor (UV sensor) is processed by the
control system in two different ways; as analog output and digital
output. Digital signals are used to detect a flame and are involved
in the control panel logics, while analog signals are not used.
Field testing and several tests have shown a high degree of
variability and low repeatability of flame detector signals, which
are the cause of "false" loss-of-flame on secondary and trips,
running in Premix mode. The health of the flame detector depends on
many factors including air humidity, dirt accumulate on the lens
and electric wire connections. In the real-time DLN and diffusion
combustion rule set, the analog output is used to monitor the
secondary flame detectors: each signal is processed using a
statistical approach to identify the noise and variation and
generate a "health count metric". This metric is used to define
thresholds and indicate if it is needed to change or tune the
sensors. The output recommendation is to either replace, tune,
check, or clean the lens of the detector. The flame detector rule
of the real-time DLN and diffusion combustion rule set monitors
degradation over time and, thus, can predict early signs of
failure. The output recommendations can distinguish a deteriorating
detector from a dirty or foggy one.
[0026] 4. Unnecessary unloading and excess flaring is currently
needed to transfer from the Extended Lean-Lean (EXT-LL) mode to
Premix mode. Hence, any benefits associated with low emissions are
contradicted by this excess flare. Based on a fuel gas line
pressure drop calculation, the real-time DLN and diffusion
combustion rule set evaluates the possibility of transferring
directly without unloading, which can reduce flaring and allowing
the transfer without reducing gas turbine load. The DLN transfer
rule allows operators to understand the possibility of avoiding
unnecessary unloading to save time, fuel and emissions resulting
from excess process gas flaring.
[0027] FIG. 1 is a schematic block diagram of remote monitoring and
diagnostic system 100 in accordance with an exemplary embodiment of
the present invention. In the exemplary embodiment, system 100
includes a remote monitoring and diagnostic center 102. Remote
monitoring and diagnostic center 102 is operated by an entity, such
as, an OEM of a plurality of equipment purchased and operated by a
separate business entity, such as, an operating entity. In the
exemplary embodiment, the OEM and operating entity enter into a
support arrangement whereby the OEM provides services related to
the purchased equipment to the operating entity. The operating
entity may own and operate purchased equipment at a single site or
multiple sites. Moreover, the OEM may enter into support
arrangements with a plurality of operating entities, each operating
their own single site or multiple sites. The multiple sites each
may contain identical individual equipment or pluralities of
identical sets of equipment, such as trains of equipment.
Additionally, at least some of the equipment may be unique to a
site or unique to all sites.
[0028] In the exemplary embodiment, a first site 104 includes one
or more process analyzers 106, equipment monitoring systems 108,
equipment local control centers 110, and/or monitoring and alarm
panels 112 each configured to interface with respective equipment
sensors and control equipment to effect control and operation of
the respective equipment. The one or more process analyzers 106,
equipment monitoring systems 108, equipment local control centers
110, and/or monitoring and alarm panels 112 are communicatively
coupled to an intelligent monitoring and diagnostic system 114
through a network 116. Intelligent monitoring and diagnostic (IMAD)
system 114 is further configured to communicate with other on-site
systems (not shown in FIG. 1) and offsite systems, such as, but not
limited to, remote monitoring and diagnostic center 102. In various
embodiments, IMAD 114 is configured to communicate with remote
monitoring and diagnostic center 102 using for example, a dedicated
network 118, a wireless link 120, and the Internet 122.
[0029] Each of a plurality of other sites, for example, a second
site 124 and an nth site 126 may be substantially similar to first
site 104 although may or may not be exactly similar to first site
104.
[0030] FIG. 2 is a block diagram of an exemplary embodiment of a
network architecture 200 of a local industrial plant monitoring and
diagnostic system, such as a distributed control system (DCS) 201.
The industrial plant may include a plurality of plant equipment,
such as gas turbines, centrifugal compressors, gearboxes,
generators, pumps, motors, fans, and process monitoring sensors
that are coupled in flow communication through interconnecting
piping, and coupled in signal communication with DCS 201 through
one or more remote input/output (I/O) modules and interconnecting
cabling and/or wireless communication. In the exemplary embodiment,
the industrial plant includes DCS 201 including a network backbone
203. Network backbone 203 may be a hardwired data communication
path fabricated from twisted pair cable, shielded coaxial cable or
fiber optic cable, for example, or may be at least partially
wireless. DCS 201 may also include a processor 205 that is
communicatively coupled to the plant equipment, located at the
industrial plant site or at remote locations, through network
backbone 203. It is to be understood that any number of machines
may be operatively connected to network backbone 203. A portion of
the machines may be hardwired to network backbone 203, and another
portion of the machines may be wirelessly coupled to backbone 203
via a wireless base station 207 that is communicatively coupled to
DCS 201. Wireless base station 207 may be used to expand the
effective communication range of DCS 201, such as with equipment or
sensors located remotely from the industrial plant but, still
interconnected to one or more systems within the industrial
plant.
[0031] DCS 201 may be configured to receive and display operational
parameters associated with a plurality of equipment, and to
generate automatic control signals and receive manual control
inputs for controlling the operation of the equipment of industrial
plant. In the exemplary embodiment, DCS 201 may include a software
code segment configured to control processor 205 to analyze data
received at DCS 201 that allows for on-line monitoring and
diagnosis of the industrial plant machines. Data may be collected
from each machine, including gas turbines, centrifugal compressors,
pumps and motors, associated process sensors, and local
environmental sensors including, for example, vibration, seismic,
temperature, pressure, current, voltage, ambient temperature and
ambient humidity sensors. The data may be pre-processed by a local
diagnostic module or a remote input/output module, or may
transmitted to DCS 201 in raw form.
[0032] A local monitoring and diagnostic system (LMDS) 213 may be a
separate add-on hardware device, such as, for example, a personal
computer (PC), that communicates with DCS 201 and other control
systems 209 and data sources through network backbone 203. LMDS 213
may also be embodied in a software program segment executing on DCS
201 and/or one or more of the other control systems 209.
Accordingly, LMDS 213 may operate in a distributed manner, such
that a portion of the software program segment executes on several
processors concurrently. As such, LMDS 213 may be fully integrated
into the operation of DCS 201 and other control systems 209. LMDS
213 analyzes data received by DCS 201, data sources, and other
control systems 209 to determine an operational health of the
machines and/or a process employing the machines using a global
view of the industrial plant.
[0033] In the exemplary embodiment, network architecture 100
includes a server grade computer 202 and one or more client systems
203. Server grade computer 202 further includes a database server
206, an application server 208, a web server 210, a fax server 212,
a directory server 214, and a mail server 216. Each of servers 206,
208, 210, 212, 214, and 216 may be embodied in software executing
on server grade computer 202, or any combinations of servers 206,
208, 210, 212, 214, and 216 may be embodied alone or in combination
on separate server grade computers coupled in a local area network
(LAN) (not shown). A data storage unit 220 is coupled to server
grade computer 202. In addition, a workstation 222, such as a
system administrator's workstation, a user workstation, and/or a
supervisor's workstation are coupled to network backbone 203.
Alternatively, workstations 222 are coupled to network backbone 203
using an Internet link 226 or are connected through a wireless
connection, such as, through wireless base station 207.
[0034] Each workstation 222 may be a personal computer having a web
browser. Although the functions performed at the workstations
typically are illustrated as being performed at respective
workstations 222, such functions can be performed at one of many
personal computers coupled to network backbone 203. Workstations
222 are described as being associated with separate exemplary
functions only to facilitate an understanding of the different
types of functions that can be performed by individuals having
access to network backbone 203.
[0035] Server grade computer 202 is configured to be
communicatively coupled to various individuals, including employees
228 and to third parties, e.g., service providers 230. The
communication in the exemplary embodiment is illustrated as being
performed using the Internet, however, any other wide area network
(WAN) type communication can be utilized in other embodiments,
i.e., the systems and processes are not limited to being practiced
using the Internet.
[0036] In the exemplary embodiment, any authorized individual
having a workstation 232 can access LMDS 213. At least one of the
client systems may include a manager workstation 234 located at a
remote location. Workstations 222 may be embodied on personal
computers having a web browser. Also, workstations 222 are
configured to communicate with server grade computer 202.
Furthermore, fax server 212 communicates with remotely located
client systems, including a client system 236 using a telephone
link (not shown). Fax server 212 is configured to communicate with
other client systems 228, 230, and 234, as well.
[0037] Computerized modeling and analysis tools of LMDS 213, as
described below in more detail, may be stored in server 202 and can
be accessed by a requester at any one of client systems 204. In one
embodiment, client systems 204 are computers including a web
browser, such that server grade computer 202 is accessible to
client systems 204 using the Internet. Client systems 204 are
interconnected to the Internet through many interfaces including a
network, such as a local area network (LAN) or a wide area network
(WAN), dial-in-connections, cable modems and special high-speed
ISDN lines. Client systems 204 could be any device capable of
interconnecting to the Internet including a web-based phone,
personal digital assistant (PDA), or other web-based connectable
equipment. Database server 206 is connected to a database 240
containing information about industrial plant 10, as described
below in greater detail. In one embodiment, centralized database
240 is stored on server grade computer 202 and can be accessed by
potential users at one of client systems 204 by logging onto server
grade computer 202 through one of client systems 204. In an
alternative embodiment, database 240 is stored remotely from server
grade computer 202 and may be non-centralized.
[0038] Other industrial plant systems may provide data that is
accessible to server grade computer 202 and/or client systems 204
through independent connections to network backbone 204. An
interactive electronic tech manual server 242 services requests for
machine data relating to a configuration of each machine. Such data
may include operational capabilities, such as pump curves, motor
horsepower rating, insulation class, and frame size, design
parameters, such as dimensions, number of rotor bars or impeller
blades, and machinery maintenance history, such as field
alterations to the machine, as-found and as-left alignment
measurements, and repairs implemented on the machine that do not
return the machine to its original design condition.
[0039] A portable vibration monitor 244 may be intermittently
coupled to LAN directly or through a computer input port such as
ports included in workstations 222 or client systems 204.
Typically, vibration data is collected in a route, collecting data
from a predetermined list of machines on a periodic basis, for
example, monthly or other periodicity. Vibration data may also be
collected in conjunction with troubleshooting, maintenance, and
commissioning activities. Further, vibration data may be collected
continuously in a real-time or near real-time basis. Such data may
provide a new baseline for algorithms of LMDS 213. Process data may
similarly, be collected on a route basis or during troubleshooting,
maintenance, and commissioning activities. Moreover, some process
data may be collected continuously in a real-time or near real-time
basis. Certain process parameters may not be permanently
instrumented and a portable process data collector 245 may be used
to collect process parameter data that can be downloaded to DCS 201
through workstation 222 so that it is accessible to LMDS 213. Other
process parameter data, such as process fluid composition analyzers
and pollution emission analyzers may be provided to DCS 201 through
a plurality of on-line monitors 246.
[0040] Electrical power supplied to various machines or generated
by generated by generators with the industrial plant may be
monitored by a motor protection relay 248 associated with each
machine. Typically, such relays 248 are located remotely from the
monitored equipment in a motor control center (MCC) or in
switchgear 250 supplying the machine. In addition, to protection
relays 248, switchgear 250 may also include a supervisory control
and data acquisition system (SCADA) that provides LMDS 213 with
power supply or power delivery system (not shown) equipment located
at the industrial plant, for example, in a switchyard, or remote
transmission line breakers and line parameters.
[0041] FIG. 3 is a block diagram of an exemplary rule set 280 that
may be used with LMDS 213 (shown in FIG. 1). Rule set 280 may be a
combination of one or more custom rules, and a series of properties
that define the behavior and state of the custom rules. The rules
and properties may be bundled and stored in a format of an XML
string, which may be encrypted based on a 25 character alphanumeric
key when stored to a file. Rule set 280 is a modular knowledge cell
that includes one or more inputs 282 and one or more outputs 284.
Inputs 282 may be software ports that direct data from specific
locations in LMDS 213 to rule set 280. For example, an input from a
pump outboard vibration sensor may be transmitted to a hardware
input termination in DCS 201. DCS 201 may sample the signal at that
termination to receive the signal thereon. The signal may then be
processed and stored at a location in a memory accessible and/or
integral to DCS 201. A first input 286 of rule set 280 may be
mapped to the location in memory such that the contents of the
location in memory is available to rule set 280 as an input.
Similarly, an output 288 may be mapped to another location in the
memory accessible to DCS 201 or to another memory such that the
location in memory contains the output 288 of rule set 280.
[0042] In the exemplary embodiment, rule set 280 includes one or
more rules relating to monitoring and diagnosis of specific
problems associated with equipment operating in an industrial
plant, such as, for example, a gas reinjection plant, a liquid
natural gas (LNG) plant, a power plant, a refinery, and a chemical
processing facility. Although rule set 280 is described in terms of
being used with an industrial plant, rule set 280 may be
appropriately constructed to capture any knowledge and be used for
determining solutions in any field. For example, rule set 280 may
contain knowledge pertaining to economic behavior, financial
activity, weather phenomenon, and design processes. Rule set 280
may then be used to determine solutions to problems in these
fields. Rule set 280 includes knowledge from one or many sources,
such that the knowledge is transmitted to any system where rule set
280 is applied. Knowledge is captured in the form of rules that
relate outputs 284 to inputs 282 such that a specification of
inputs 282 and outputs 284 allows rule set 280 to be applied to
LMDS 213. Rule set 280 may include only rules specific to a
specific plant asset and may be directed to only one possible
problem associated with that specific plant asset. For example,
rule set 280 may include only rules that are applicable to a motor
or a motor/pump combination. Rule set 280 may only include rules
that determine a health of the motor/pump combination using
vibration data. Rule set 280 may also include rules that determine
the health of the motor/pump combination using a suite of
diagnostic tools that include, in addition to vibration analysis
techniques, but may also include, for example, performance
calculational tools and/or financial calculational tools for the
motor/pump combination.
[0043] In operation, rule set 280 is created in a software
developmental tool that prompts a user for relationships between
inputs 282 and outputs 284. Inputs 282 may receive data
representing, for example digital signals, analog signals,
waveforms, processed signals, manually entered and/or configuration
parameters, and outputs from other rule sets. Rules within rule set
280 may include logical rules, numerical algorithms, application of
waveform and signal processing techniques, expert system and
artificial intelligence algorithms, statistical tools, and any
other expression that may relate outputs 284 to inputs 282. Outputs
284 may be mapped to respective locations in the memory that are
reserved and configured to receive each output 284. LMDS 213 and
DCS 201 may then use the locations in memory to accomplish any
monitoring and/or control functions LMDS 213 and DCS 201 may be
programmed to perform. The rules of rule set 280 operate
independently of LMDS 213 and DCS 201, although inputs 282 may be
supplied to rule set 280 and outputs 284 may be supplied to rule
set 280, directly or indirectly through intervening devices.
[0044] During creation of rule set 280, a human expert in the field
divulges knowledge of the field particular to a specific asset
using a development tool by programming one or more rules. The
rules are created by generating expressions of relationship between
outputs 284 and inputs 282. Operands may be selected from a library
of operands, using graphical methods, for example, using drag and
drop on a graphical user interface built into the development tool.
A graphical representation of an operand may be selected from a
library portion of a screen display (not shown) and dragged and
dropped into a rule creation portion. Relationships between input
282 and operands are arranged in a logical display fashion and the
user is prompted for values, such as, constants, when appropriate
based on specific operands and specific ones of inputs 282 that are
selected. As many rules that are needed to capture the knowledge of
the expert are created. Accordingly, rule set 280 may include a
robust set of diagnostic and/or monitoring rules or a relatively
less robust set of diagnostic and/or monitoring rules based on a
customer's requirements and a state of the art in the particular
field of rule set 280. The development tool provides resources for
testing rule set 280 during the development to ensure various
combinations and values of inputs 282 produce expected outputs at
outputs 284.
[0045] As described below, rule sets are defined to assess exhaust
temperature spread as a function of combustion mode and load, a
swirl angle calculation to trace back the exhaust temperature
spread to a source or a faulty combustor(s), the health of flame
detectors and generates warnings and recommendations to avoid false
loss-of-flame alarms and trips, unnecessary unloading and excess
flaring currently needed to transfer from the Extended Lean-Lean
(EXT-LL) mode to Premix mode of gas turbine operation.
[0046] FIG. 4 is a side elevation view of a gas turbine engine 400
in accordance with an exemplary embodiment of the present
disclosure. In the exemplary embodiment, gas turbine engine 400
includes a plurality of partialized combustion chambers 402
positioned in flow communication with a downstream low pressure or
load turbine 404, and a diffuser 406 positioned downstream of low
pressure turbine 404. Diffuser 406 includes a plurality of
thermocouples 408 positioned about an interior of diffuser 406 in a
flowpath of exhaust gases exiting low-pressure turbine 404. In the
exemplary embodiment, thermocouples 408 number thirteen, which are
evenly spaced circumferentially about diffuser 406. In various
embodiments, other numbers of thermocouples 408 are used, which may
be spaced as is convenient in diffuser 406.
[0047] In the exemplary embodiment, thermocouples 408 are
communicatively coupled to high spread detector 410, which is
configured to receive temperature signals and to apply one or more
exhaust spread detection rule sets to the signals. The partialized
combustion chambers 402 are spaced circumferentially about gas
turbine engine 400. The exhaust gases exiting each combustion
chamber 402 vary in temperature based on combustion conditions
within each combustion chamber 402. The exhaust gases of each
combustion chamber 402 tend to mix only somewhat with the exhaust
gases exiting others of the plurality of combustion chambers 402.
Depending on the gas turbine engine operating conditions, including
but not limited to load, airflow, and combustion chamber 402
operating condition, each thermocouple 408 may be closely
associated with a discernible one or more of combustion chambers
402. Such close association permits a detection of a problem with a
burner in one of combustion chambers 402 by detecting anomalies in
the temperature spread in diffuser 406 as sensed by thermocouples
408.
[0048] An exhaust spread rule set associated with high spread
detector 410 evaluates swirl angle, which, as used herein, refers
to the angle between the measured representative exhaust gas
temperature, at varying loads, and the combustion chamber 402
source-location. In the exemplary embodiment, the exhaust spread
rule set is a transfer function with the following inputs:
[0049] Exhaust temperature thermocouples readings (TTXD.sub.--1, .
. . TTXD.sub.--13*)
[0050] Exhaust temperature spread (TTXSP1*)
[0051] High pressure turbine speed--percentage (TNH*)
[0052] Low pressure turbine speed--percentage (TNL*)
[0053] Absolute Pressure compressor discharge (PCD_abs*)
[0054] Ambient pressure (AFPAP*)
[0055] The exhaust spread rule set is configured to output a swirl
angle and a cold/hot spots evaluation. The output is used to
identify a location of a probable cause of temperature spread
around diffuser 406. The exhaust spread rule set is configured to
output steps to be performed for troubleshooting when a swirl angle
that exceeds a predetermined threshold range or when another
indicator of temperature spread anomaly is detected. For example,
the exhaust spread rule set may output troubleshooting steps that
include for example, 1. Correctly identify the hot and cold spots
in the exhaust temperature profile, 2. Trace the exhaust
temperature anomaly through the gas swirl angle to a particular
combustion chamber location, 3. Identify hardware which is capable
of producing a variation in the combustion pattern.
[0056] The applied methodology of the exhaust spread rule set
includes evaluating the presence of a cold/hot spot, locating the
cold/hot region, selecting the coldest/hottest thermocouples and
its corresponding location in the exhaust plenum, perform a check
of adjacent thermocouples, calculating the swirl angle using the
exhaust spread rule set transfer function, from the location of the
low thermocouple, back-trace the amount of the swirl angle to
identify the location of the probable cause.
[0057] FIG. 5 is a schematic representation of the placement of
twelve thermocouples 408 spaced approximately evenly about diffuser
406 in accordance with an exemplary embodiment of the present
disclosure. A flow of exhaust gases through diffuser 406 would be
oriented into or out of the page on FIG. 5. Based on each
thermocouples 408 fixed position in diffuser 406 a relationship
between the temperatures sensed by each of thermocouples 408 and
associated combustion chambers 402 may be determined and monitored.
An uncertainty band 500 may be used to describe a relative
uncertainty of the determined swirl angle. Such uncertainty may be
affected by for example, load on gas turbine engine 400.
[0058] FIG. 6 is a graph 550 illustrating a correlation between
burner clogging and exhaust temperature spread. Graph 550 include a
an x-axis 552 graduated in units of % burner clogging and a y-axis
554 graduated in units of temperature of the exhaust spread. A
trace 556 is a curve-fit over several data points from field
analysis illustrating the correlation between burner clogging and
exhaust temperature spread.
[0059] The temperature spread at the exit of the combustion
chambers 402 is a function of for example, but not limited to the
combustion mode of gas turbine engine 400, a fuel split, and a
power output of gas turbine engine 400. The DLN-1 combustion
monitoring rule set is a simple rule based on a predetermined
threshold range.
[0060] The DLN-1 combustion monitoring rule set receives as
inputs:
[0061] Combustion mode (DLN_MODE_GAS*)
[0062] Average exhaust temperature (TTXM*)
[0063] Exhaust temperature spread (TTXSP1*)
[0064] Exhaust temperature spread limit (TTXSPL*)
[0065] Combustion reference temperature (CTF*)
[0066] Exhaust temperature thermocouples readings (TTXD.sub.--1, .
. . TTXD.sub.--13*)
[0067] The threshold used to signal a monitoring anomaly depends
primarily on the combustion mode and gas turbine engine load. For
example:
[0068] Warm-Up: 60.degree. F.
[0069] Primary Mode: 45.degree. F.
[0070] Lean-Lean Mode: (TTXM-CTF)*0.075+30.degree. F.
[0071] Premix-Steady State Mode: 75.degree. F.
[0072] Extended-Lean Lean Mode load: 80.degree. F.
[0073] The DLN-1 combustion monitoring rule set outputs alarms,
indications, such as, but not limited to, check for broken
thermocouple or check for plugged burners. The DLN-1 combustion
monitoring rule set also outputs steps for troubleshooting, for
example:
[0074] 1. Correctly identify the hot and cold spots in the exhaust
temperature profile
[0075] 2. Trace the exhaust temperature anomaly through a known
threshold
[0076] 3. Investigate primary and secondary burner involvement
[0077] The applied methodology of the DLN-1 combustion monitoring
rule set includes locating the cold region by analyzing the exhaust
temperature data, selecting the coldest/hottest thermocouples and
its corresponding location in the exhaust plenum, evaluating the
presence of a cold/hot spot, detect any sudden spread increase
higher than 25.degree. F., calculating (S1) Spread#1
(TTXSP1)=hottest-coldest thermocouple temperature, (S2) Spread#2
(TTXSP2)=hottest-2nd coldest thermocouple temperature, checking
adjacent thermocouple for consistency, recording spreads in
relevant conditions (Primary HL, Secondary, . . . ), defining
threshold from DLN-1 Combustor good practice, and comparing both
spreads with the given threshold.
[0078] FIG. 7 is a schematic block diagram of a flame detector (FD)
circuit 600 that may be used with gas turbine engine 400 (shown in
FIG. 4) in accordance with an exemplary embodiment of the present
disclosure. In the exemplary embodiment, flame detector circuit 600
may be used with a flame detection rule set to provide an
indication of the health, sensitivity, and operability of the flame
detectors (not shown), which leads to a reduced occurrence of trip
due to instrumentation failure. The rule set associated with a
secondary FDs sensitivity check is a simple rule set based on
values for monitored parameters being within a predetermined
threshold.
[0079] The inputs to the FD rule set include:
[0080] FDs analog signals (fd_intens.sub.--1, . . .
fd_intens.sub.--8)
[0081] FDs logical signals (L28FDA, . . . L28FDH)
[0082] Relative humidity signal (CMHUM)
[0083] The output of the FD rule set includes alarms, such as, but
not limited to "Flame detectors changing" and "Flame detector to be
tuned."
[0084] In the exemplary embodiment, a raw pulse signal from a flame
sensor is processed by the FD rule set in two different ways, the
analog outputs (FD_INTENS_n) 602 are frequency outputs generated by
using a fixed time window of one second for monitoring purposes.
The digital output (L28FDn) 604 is generated by comparing a
frequency output based on a different time window, for example,
1/16 second with the corresponding count thresholds set-up in the
control system's interfaces flame-on/flame-off logic.
[0085] FIG. 8 is a screen capture 700 of a trace of analog outputs
602 and digital output 604. Detection levels, and detection time
are the control parameters used for FD threshold tuning. The
frequency threshold level is calculated and defined by:
[0086] Detection level=14, (frequency threshold=87.5 Hz), digital
signal is flat and equal to 1.
[0087] Detection level=16, (frequency threshold=100 Hz), digital
signal begins to flicker, switching from 0 to 1.
[0088] Detection level=18, (frequency threshold=112.5 Hz), digital
signal flickering.
[0089] Detection level=20, (frequency threshold=120 Hz), residual
spike of L28fdf
[0090] Detection level=22, (frequency level=137.5 Hz), digital
signal is flat and equal to 0.
[0091] From analysis performed on several field data, for each
secondary flame sensor the following condition is used:
[0092] If: (Avg-7*STDV.sub.calculated)*detection time ( 1/16
s)<1--the flame detector will be replaced.
[0093] If: (Avg-7*STDV.sub.calculated)*detection time ( 1/16
s)<2--the flame detector will be tuned.
[0094] FIG. 9 is a flow diagram 900 of operation of gas turbine
engine 400 during a loading and an unloading process. An axis 902
indicates GT load for the loading operating area 904 and unloading
operating area 906. Arrows indicate a path gas turbine engine 400
may take in traversing the operating areas. A direct transfer rule
set is used to calculate the possibility of transferring directly
from EXT-LL mode of operation directly into the PREMIX mode of
operation.
[0095] In the exemplary embodiment, direct transfer rule set is a
transfer function type rule set. Direct transfer rule set receives
as inputs:
[0096] Fuel gas pressure upstream SVR
[0097] Intervalve pressure (FPG2*)
[0098] Compressor discharge pressure (PCD*)
[0099] Ambient pressure (AFPAP*)
[0100] Fuel gas temperature (FGT2*)
[0101] Gas control valve (GCV), Stop-Ratio Valve (SRV), Gas control
valve (GCV) characterization--kv and Xt
[0102] Secondary burner effective area
[0103] Direct transfer rule set outputs:
[0104] Pressure downstream GCV
[0105] Fuel gas flow estimation
[0106] Indication of unit capability to transfer directly from
EXT-LL into PREMIX
[0107] DLN-1 operation, from start-up to full load, involves five
different modes of combustion in the multi-zone combustion liner.
The distribution of the fuel and flame to the different zones is
matched to turbine speed and load conditions to obtain the best
performance and emissions with stable flames operation.
[0108] If the unit is running in EXTENDED LEAN-LEAN, with the
Current DLN-1 logic in order to get PREMIX STEADY STATE it is
necessary to:
[0109] Unload the unit below .about.40% Base Load*, transferring
back into LEAN-LEAN POSITIVE.
[0110] Transfer into PREMIX STEADY-STATE by increasing load.
[0111] Moreover the ignition transformer protection logic
introduces another limitation inhibiting PREMIX transfer-in, if the
transformer duty cycle is exceeded.
[0112] FIG. 10 is a schematic piping diagram of a portion of a fuel
system 1000 that may be used with gas turbine engine 400 (shown in
FIG. 4) in accordance with an exemplary embodiment of the present
disclosure.
[0113] The DLN-1 capability of transfer into PREMIX is related to
the ability of maintaining choking condition on a GCV valve 1002
during SECONDARY transfer mode.
[0114] GCV upstream pressure 1004 and SRV upstream pressure 1006
are defined in order to feed all the amount of gas into a
"Transferless" secondary fuel nozzle 1008, without drops in unit
load during SECONDARY transfer mode.
[0115] The condition for having a good transfer into PREMIX mode
can be calculated in real time in order to identify an enlarged
window for PREMIX availability including a direct transfer from
EXT-LL to PREMIX.
[0116] Direct transfer EXT-LL PREMIX--rule development includes
[0117] 1st Step--Fuel mass flow calculation.
[0118] Assuming the gas control valve (GCV) choked and N=1:
? = ( ? ? ) ? = 1.23 ##EQU00001## M = ? ? ? ? ? ( ? ) ? , ?
indicates text missing or illegible when filed ##EQU00001.2##
[0119] where
[0120] k=cp/cv is the one of the leanest gas from job CSO
[0121] R is the one of the leanest gas from fuel job CSO
[0122] A.sub.ev=effective area as a function of stroke (from table
or correlations)
[0123] 2.sup.nd STEP--Primary fuel nozzle pressure [P8] 1010
calculation, when only secondary nozzle is fed.
[0124] P.sub.CC=PCD (1-PLF)--with PLF .about.4%
? ( ? , [ 1 - ( ? ] = ? ? , ? indicates text missing or illegible
when filed ##EQU00002##
where:
[0125] T8=FGT fuel gas temperature
[0126] R is the one of the leanest gas
[0127] Aeff=effective area as a function of pressure ratio across
the burner
[0128] k=cp/cv is the one of the leanest gas from job CSO
[0129] R is the one of the leanest gas of the leanest gas from job
CSO
[0130] 3.sup.rd STEP--GCV downstream 1012 calculation, when only
secondary nozzle is fed and P7.about.P8.
? = ? { ? ? ? = k 1.4 ? indicates text missing or illegible when
filed ##EQU00003##
[0131] Where:
[0132] Cv=at 0% GSV opening
[0133] k=cp/cv is the one of the leanest gas from job CSO
[0134] Sg is the one of the leanest gas from fuel job CSO
[0135] 4.sup.th STEP--GCV choking verification
[0136] If,
( ? ) > 1.23 , ? indicates text missing or illegible when filed
##EQU00004##
then the unit is able to transfer into PREMIX aside from EXT-LL
mode.
[0137] The logic flows depicted in the figures do not require the
particular order shown, or sequential order, to achieve desirable
results. In addition, other steps may be provided, or steps may be
eliminated, from the described flows, and other components may be
added to, or removed from, the described systems. Accordingly,
other embodiments are within the scope of the following claims.
[0138] It will be appreciated that the above embodiments that have
been described in particular detail are merely example or possible
embodiments, and that there are many other combinations, additions,
or alternatives that may be included.
[0139] Also, the particular naming of the components,
capitalization of terms, the attributes, data structures, or any
other programming or structural aspect is not mandatory or
significant, and the mechanisms that implement the invention or its
features may have different names, formats, or protocols. Further,
the system may be implemented via a combination of hardware and
software, as described, or entirely in hardware elements. Also, the
particular division of functionality between the various system
components described herein is merely one example, and not
mandatory; functions performed by a single system component may
instead be performed by multiple components, and functions
performed by multiple components may instead performed by a single
component.
[0140] Some portions of above description present features in terms
of algorithms and symbolic representations of operations on
information. These algorithmic descriptions and representations may
be used by those skilled in the data processing arts to most
effectively convey the substance of their work to others skilled in
the art. These operations, while described functionally or
logically, are understood to be implemented by computer programs.
Furthermore, it has also proven convenient at times, to refer to
these arrangements of operations as modules or by functional names,
without loss of generality.
[0141] Unless specifically stated otherwise as apparent from the
above discussion, it is appreciated that throughout the
description, discussions utilizing terms such as "processing" or
"computing" or "calculating" or "determining" or "displaying" or
"providing" or the like, refer to the action and processes of a
computer system, or similar electronic computing device, that
manipulates and transforms data represented as physical
(electronic) quantities within the computer system memories or
registers or other such information storage, transmission or
display devices.
[0142] While the disclosure has been described in terms of various
specific embodiments, it will be recognized that the disclosure can
be practiced with modification within the spirit and scope of the
claims.
[0143] The term processor, as used herein, refers to central
processing units, microprocessors, microcontrollers, reduced
instruction set circuits (RISC), application specific integrated
circuits (ASIC), logic circuits, and any other circuit or processor
capable of executing the functions described herein.
[0144] As used herein, the terms "software" and "firmware" are
interchangeable, and include any computer program stored in memory
for execution by processor 205, including RAM memory, ROM memory,
EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory.
The above memory types are exemplary only, and are thus not
limiting as to the types of memory usable for storage of a computer
program.
[0145] As will be appreciated based on the foregoing specification,
the above-described embodiments of the disclosure may be
implemented using computer programming or engineering techniques
including computer software, firmware, hardware or any combination
or subset thereof, wherein the technical effect includes (a)
storing a plurality rule sets in the memory device, the rule sets
relative to the operation of the gas turbine, the rule sets
including at least one rule expressed as a relational expression of
a real-time data output relative to a real-time data input, the
relational expression being specific to at least one of a
temperature spread of an exhaust flow of the gas turbine, a swirl
angle of the exhaust flow, a health of a plurality of flame
detectors of the gas turbine, and a transfer of the gas turbine
from a first mode of operation to a second lower NOX mode of
operation, (b) receiving real-time and historical data inputs from
a condition monitoring system associated with the gas turbine, the
data inputs relating to parameters affecting at least one of the
temperature spread of the exhaust flow of the gas turbine, the
swirl angle of the exhaust flow, the health of the plurality of
flame detectors of the gas turbine, and the transfer of the gas
turbine from the first mode of operation to the second lower NOX
mode of operation, (c) determining a fuel gas line pressure drop
using the received data, (d) comparing the determined pressure drop
to a predetermined threshold range; and (e) recommending to an
operator of the gas turbine to transfer the mode of operation of
the gas turbine from the first mode to the second mode without
reducing a load of the gas turbine if the determined pressure drop
meets the predetermined threshold range. Any such resulting
program, having computer-readable code means, may be embodied or
provided within one or more computer-readable media, thereby making
a computer program product, i.e., an article of manufacture,
according to the discussed embodiments of the disclosure. The
computer readable media may be, for example, but is not limited to,
a fixed (hard) drive, diskette, optical disk, magnetic tape,
semiconductor memory such as read-only memory (ROM), and/or any
transmitting/receiving medium such as the Internet or other
communication network or link. The article of manufacture
containing the computer code may be made and/or used by executing
the code directly from one medium, by copying the code from one
medium to another medium, or by transmitting the code over a
network.
[0146] Many of the functional units described in this specification
have been labeled as modules, in order to more particularly
emphasize their implementation independence. For example, a module
may be implemented as a hardware circuit comprising custom very
large scale integration ("VLSI") circuits or gate arrays,
off-the-shelf semiconductors such as logic chips, transistors, or
other discrete components. A module may also be implemented in
programmable hardware devices such as field programmable gate
arrays (FPGAs), programmable array logic, programmable logic
devices (PLDs) or the like.
[0147] Modules may also be implemented in software for execution by
various types of processors. An identified module of executable
code may, for instance, comprise one or more physical or logical
blocks of computer instructions, which may, for instance, be
organized as an object, procedure, or function. Nevertheless, the
executables of an identified module need not be physically located
together, but may comprise disparate instructions stored in
different locations which, when joined logically together, comprise
the module and achieve the stated purpose for the module.
[0148] A module of executable code may be a single instruction, or
many instructions, and may even be distributed over several
different code segments, among different programs, and across
several memory devices. Similarly, operational data may be
identified and illustrated herein within modules, and may be
embodied in any suitable form and organized within any suitable
type of data structure. The operational data may be collected as a
single data set, or may be distributed over different locations
including over different storage devices, and may exist, at least
partially, merely as electronic signals on a system or network.
[0149] The above-described embodiments of a method and monitoring
and diagnostic system for a gas turbine that includes a rule module
provides a cost-effective and reliable means for providing
meaningful operational recommendations and troubleshooting actions.
Moreover, the system is more accurate and less prone to false
alarms. More specifically, the methods and systems described herein
can predict component failure at a much earlier stage than known
systems to facilitate significantly reducing outage time and
preventing trips. In addition, the above-described methods and
systems facilitate predicting anomalies at an early stage enabling
site personnel to prepare and plan for a shutdown of the equipment.
As a result, the methods and systems described herein facilitate
operating gas turbines and other equipment in a cost-effective and
reliable manner.
[0150] This written description uses examples to disclose the
invention, including the best mode, and also to enable any person
skilled in the art to practice the invention, including making and
using any devices or systems and performing any incorporated
methods. The patentable scope of the disclosure is defined by the
claims, and may include other examples that occur to those skilled
in the art. Such other examples are intended to be within the scope
of the claims if they have structural elements that do not differ
from the literal language of the claims, or if they include
equivalent structural elements with insubstantial differences from
the literal languages of the claims.
* * * * *