Abstract

One of the most disrupting events that affect gas turbine (GT) operation is trip, since its occurrence reduces machine life span and also causes business interruption. Thus, early detection of incipient symptoms of GT trip is crucial to ensure efficient operation and save costs. This paper presents a data-driven methodology of which the goal is the disclosure of the onset of trip symptoms by exploring multiple trigger scenarios. For each scenario, a time window of the same length is considered before and after the trigger time point: the former is supposed to be representative of normal operation and is labeled “no trip,” whereas the latter is labeled “trip.” A long short-term memory (LSTM) neural network is first trained for each scenario and subsequently tested on new trips over a timeframe of 3 days of operation before trip occurrence. Finally, trips are clustered into homogeneous groups according to their most likely trigger position, which identifies the time point of onset of trip symptoms. The methodology is applied to two real-world case studies composed of a collection of trips, of which the causes are different, taken from various fleets of GTs in operation. Data collected from multiple sensors are employed and analyzed. The methodology provides the most likely trigger position for four clusters of trips and both case studies with a confidence in the range 66–97%.

References

1.
Tahan
,
M.
,
Tsoutsanis
,
E.
,
Muhammad
,
M.
, and
Abdul Karim
,
Z. A.
,
2017
, “
Performance-Based Health Monitoring, Diagnostics and Prognostics for Condition-Based Maintenance of Gas Turbines: A Review
,”
Appl. Energy
,
198
, pp.
122
144
.10.1016/j.apenergy.2017.04.048
2.
Wen
,
Y.
,
Rahman
,
M. F.
,
Xu
,
H.
, and
Tseng
,
T.-L. B.
,
2022
, “
Recent Advances and Trends of Predictive Maintenance From Data-Driven Machine Prognostics Perspective
,”
Measurement
,
187
, p.
110276
.10.1016/j.measurement.2021.110276
3.
Salilew
,
W. M.
,
Abdul Karim
,
Z. A.
, and
Baheta
,
A. T.
,
2021
, “
Review on Gas Turbine Condition Based Diagnosis Method
,”
Mater. Today: Proc.
, epub.10.1016/j.matpr.2020.12.1049
4.
Ceschini
,
G. F.
,
Gatta
,
N.
,
Venturini
,
M.
,
Hubauer
,
T.
, and
Murarasu
,
A.
,
2018
, “
A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)
,”
ASME J. Eng. Gas Turbines Power
,
140
(
3
), p.
032402
. 10.1115/1.4037964
5.
Manservigi
,
L.
,
Venturini
,
M.
,
Ceschini
,
G. F.
,
Bechini
,
G.
, and
Losi
,
E.
,
2020
, “
Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults
,”
ASME J. Eng. Gas Turbines Power
,
142
(
2
), p. 0
21009
. 10.1115/1.4045711
6.
Losi
,
E.
,
Venturini
,
M.
,
Manservigi
,
L.
,
Ceschini
,
G. F.
, and
Bechini
,
G.
,
2019
, “
Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models
,”
ASME J. Eng. Gas Turbines Power
,
141
(
11
), p.
111019
.10.1115/1.4044781
7.
Puggina
,
N.
, and
Venturini
,
M.
,
2012
, “
Development of a Statistical Methodology for Gas Turbine Prognostics
,”
ASME J. Eng. Gas Turbines Power
,
134
(
2
), p. 0
22401
.10.1115/1.4004185
8.
Cavarzere
,
A.
, and
Venturini
,
M.
,
2012
, “
Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time
,”
ASME J. Eng. Gas Turbines Power
,
134
(
1
), p. 0
12401
.10.1115/1.4004184
9.
Venturini
,
M.
, and
Puggina
,
N.
,
2012
, “
Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics
,”
ASME J. Eng. Gas Turbines Power
,
134
(
10
), p.
101601
.10.1115/1.4007064
10.
Venturini
,
M.
, and
Therkorn
,
D.
,
2013
, “
Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data
,”
ASME J. Eng. Gas Turbines Power
,
135
(
9
), p. 0
91603
.10.1115/1.4024952
11.
Gatta
,
N.
,
Venturini
,
M.
,
Manservigi
,
L.
,
Ceschini
,
G. F.
, and
Bechini
,
G.
,
2018
, “
Capability of the Bayesian Forecasting Method to Predict Field Time Series
,”
ASME J. Eng. Gas Turbines Power
,
140
(
12
), p.
121013
.10.1115/1.4040736
12.
Losi
,
E.
,
Venturini
,
M.
, and
Manservigi
,
L.
,
2019
, “
Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models
,”
ASME J. Eng. Gas Turbines Power
,
141
(
11
), p.
111018
. 10.1115/1.4044689
13.
Losi
,
E.
,
Venturini
,
M.
, and
Manservigi
,
L.
,
2020
, “
Autoregressive Bayesian Hierarchical Model to Predict Gas Turbine Degradation
,”
ASME
Paper No. GT2020-16330. 10.1115/GT2020-16330
14.
Ramesh
,
P. G.
,
Dutta
,
S. J.
,
Neog
,
S. S.
,
Baishya
,
P.
, and
Bezbaruah
,
I.
,
2020
, “
Implementation of Predictive Maintenance Systems in Remotely Located Process Plants Under Industry 4.0 Scenario
,”
Advances in RAMS Engineering
,
Springer, Cham
,
Switzerland
, pp.
293
326
.
15.
Chiang
,
L.
,
Lu
,
B.
, and
Castillo
,
I.
,
2017
, “
Big Data Analytics in Chemical Engineering
,”
Annu. Rev. Chem. Biomol. Eng.
,
8
(
1
), pp.
63
85
.10.1146/annurev-chembioeng-060816-101555
16.
Arghandeh
,
R.
, and
Zhou
,
Y.
, eds.,
2018
,
Big Data Application in Power Systems
,
Elsevier
,
Amsterdam, The Netherlands
.
17.
Musa
,
G.
,
Alrashed
,
M.
, and
Muhammad
,
N. M.
,
2021
, “
Development of Big Data Lean Optimisation Using Different Control Mode for Gas Turbine Engine Health Monitoring
,”
Energy Rep.
,
7
, pp.
4872
4881
.10.1016/j.egyr.2021.07.071
18.
Karlstetter
,
R.
,
Widhopf-Fenk
,
R.
,
Hermann
,
J.
,
Rouwenhorst
,
D.
,
Raoofy
,
A.
,
Trinitis
,
C.
, and
Schulz
,
M.
,
2019
, “
Turning Dynamic Sensor Measurements From Gas Turbines Into Insights: A Big Data Approach
,”
ASME
Paper No. GT2019-91259. 10.1115/GT2019-91259
19.
Bhargava
,
R. K.
,
2017
, Technical Dictionary on the Gas Turbine Technology,
Innovative Turbomachinery Technologies Corporation
,
Katy
,
TX
.
20.
Graichen
,
C. M.
, and
Cheetham
,
W. E.
,
2007
, “
Case-Based Reasoning Approaches for Gas Turbine Trip Diagnosis
,”
ASME
Paper No. GT2007-27856. 10.1115/GT2007-27856
21.
Ravi
,
Y. B.
,
Pandey
,
A.
, and
Jammu
,
V.
,
2010
, “
Prediction of Gas Turbine Trip Due to Electro Hydraulic Control Valve System Failures
,”
ASME
Paper No. GT2010-23228. 10.1115/GT2010-23228
22.
Losi
,
E.
,
Venturini
,
M.
,
Manservigi
,
L.
,
Ceschini
,
G. F.
,
Bechini
,
G.
,
Cota
,
G.
, and
Riguzzi
,
F.
,
2021
, “
Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series
,”
ASME J. Eng. Gas Turbines Power
,
143
(
3
), p.
031014
.10.1115/1.4049503
23.
Losi
,
E.
,
Venturini
,
M.
,
Manservigi
,
L.
,
Ceschini
,
G. F.
,
Bechini
,
G.
,
Cota
,
G.
, and
Riguzzi
,
F.
,
2021
, “
Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip
,”
ASME
Paper No. GT2021-58914. 10.1115/GT2021-58914
24.
Losi
,
E.
,
Venturini
,
M.
,
Manservigi
,
L.
,
Ceschini
,
G. F.
,
Bechini
,
G.
,
Cota
,
G.
, and
Riguzzi
,
F.
,
2022
, “
Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models
,”
ASME J. Eng. Gas Turbines Power
,
144
(
3
), p. 0
31025
. 10.1115/1.4053194
25.
Rahman
,
M. F.
,
Wen
,
Y.
,
Xu
,
H.
,
Tseng
,
T.-L. B.
, and
Akundi
,
S.
,
2020
, “
Data Mining in Telemedicine
,”
Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications
, IET Digital Library, pp.
103
131
.https://digitallibrary.theiet.org/content/books/10.1049/pbhe023e_ch6
26.
Zhang
,
Y.
,
Xiong
,
R.
,
He
,
H.
, and
Pecht
,
M. G.
,
2018
, “
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
,”
IEEE Trans. Veh. Technol.
,
67
(
7
), pp.
5695
5705
.10.1109/TVT.2018.2805189
27.
Ren
,
L.
,
Sun
,
Y.
,
Wang
,
H.
, and
Zhang
,
L.
,
2018
, “
Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network
,”
IEEE Access
,
6
, pp.
13041
13049
.10.1109/ACCESS.2018.2804930
28.
Ferguson
,
M. K.
,
Ronay
,
A.
,
Lee
,
Y.-T. T.
, and
Law
,
K. H.
,
2018
, “
Detection and Segmentation of Manufacturing Defects With Convolutional Neural Networks and Transfer Learning
,”
Smart Sustainable Manuf. Syst.
,
2
(
1
).10.1520/SSMS20180033
29.
Nguyen
,
H.-P.
,
Baraldi
,
P.
, and
Zio
,
E.
,
2021
, “
Ensemble Empirical Mode Decomposition and Long Short-Term Memory Neural Network for Multi-Step Predictions of Time Series Signals in Nuclear Power Plants
,”
Appl. Energy
,
283
, p.
116346
.10.1016/j.apenergy.2020.116346
30.
Zhao
,
R.
,
Yan
,
R.
,
Chen
,
Z.
,
Mao
,
K.
,
Wang
,
P.
, and
Gao
,
R. X.
,
2019
, “
Deep Learning and Its Applications to Machine Health Monitoring
,”
Mech. Syst. Signal Process.
,
115
, pp.
213
237
.10.1016/j.ymssp.2018.05.050
31.
Khan
,
S.
, and
Yairi
,
T.
,
2018
, “
A Review on the Application of Deep Learning in System Health Management
,”
Mech. Syst. Signal Process.
,
107
, pp.
241
265
.10.1016/j.ymssp.2017.11.024
32.
Bai
,
M.
,
Liu
,
J.
,
Chai
,
J.
,
Zhao
,
X.
, and
Yu
,
D.
,
2020
, “
Anomaly Detection of Gas Turbines Based on Normal Pattern Extraction
,”
Appl. Therm. Eng.
,
166
, p.
114664
.10.1016/j.applthermaleng.2019.114664
33.
Amozegar
,
M.
, and
Khorasani
,
K.
,
2016
, “
An Ensemble of Dynamic Neural Network Identifiers for Fault Detection and Isolation of Gas Turbine Engines
,”
Neural Networks
,
76
, pp.
106
121
.10.1016/j.neunet.2016.01.003
34.
Fentaye
,
A. D.
,
Ul-Haq Gilani
,
S. I.
,
Baheta
,
A. T.
, and
Li
,
Y.-G.
,
2019
, “
Performance-Based Fault Diagnosis of a Gas Turbine Engine Using an Integrated Support Vector Machine and Artificial Neural Network Method
,”
Proc. Inst. Mech. Eng., Part A
,
233
(
6
), pp.
786
802
.10.1177/0957650918812510
35.
Yan
,
W.
, and
Yu
,
L.
,
2015
, “
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach
,” e-print
arXiv:1908.09238
.10.48550/arXiv.1908.09238
36.
Yan
,
W.
,
2020
, “
Detecting Gas Turbine Combustor Anomalies Using Semi-Supervised Anomaly Detection With Deep Representation Learning
,”
Cognit. Comput.
,
12
(
2
), pp.
398
411
.10.1007/s12559-019-09710-7
37.
Shen
,
Y.
, and
Khorasani
,
K.
,
2020
, “
Hybrid Multi-Mode Machine Learning-Based Fault Diagnosis Strategies With Application to Aircraft Gas Turbine Engines
,”
Neural Networks
,
130
, pp.
126
142
.10.1016/j.neunet.2020.07.001
38.
Liu
,
J.
,
Liu
,
J.
,
Yu
,
D.
,
Kang
,
M.
,
Yan
,
W.
,
Wang
,
Z.
, and
Pecht
,
M. G.
,
2018
, “
Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network
,”
Energies
,
11
(
8
), p.
2149
.10.3390/en11082149
39.
Bai
,
M.
,
Yang
,
X.
,
Liu
,
J.
,
Liu
,
J.
, and
Yu
,
D.
,
2021
, “
Convolutional Neural Network-Based Deep Transfer Learning for Fault Detection of Gas Turbine Combustion Chambers
,”
Appl. Energy
,
302
, p.
117509
.10.1016/j.apenergy.2021.117509
40.
Yang
,
X.
,
Bai
,
M.
,
Liu
,
J.
,
Liu
,
J.
, and
Yu
,
D.
,
2021
, “
Gas Path Fault Diagnosis for Gas Turbine Group Based on Deep Transfer Learning
,”
Measurement
,
181
, p.
109631
.10.1016/j.measurement.2021.109631
41.
Zhong
,
S.-S.
,
Fu
,
S.
, and
Lin
,
L.
,
2019
, “
A Novel Gas Turbine Fault Diagnosis Method Based on Transfer Learning With CNN
,”
Measurement
,
137
, pp.
435
453
.10.1016/j.measurement.2019.01.022
42.
Tang
,
S.
,
Tang
,
H.
, and
Chen
,
M.
,
2019
, “
Transfer-Learning Based Gas Path Analysis Method for Gas Turbines
,”
Appl. Therm. Eng.
,
155
, pp.
1
13
.10.1016/j.applthermaleng.2019.03.156
43.
Bai
,
M.
,
Liu
,
J.
,
Ma
,
Y.
,
Zhao
,
X.
,
Long
,
Z.
, and
Yu
,
D.
,
2020
, “
Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine
,”
Energies
,
14
(
1
), p.
13
.10.3390/en14010013
44.
Zhou
,
H.
,
Ying
,
Y.
,
Li
,
J.
, and
Jin
,
Y.
,
2021
, “
Long-Short Term Memory and Gas Path Analysis Based Gas Turbine Fault Diagnosis and Prognosis
,”
Adv. Mech. Eng.
,
13
(
8
).10.1177/16878140211037767
45.
Xiang
,
S.
,
Qin
,
Y.
,
Luo
,
J.
,
Pu
,
H.
, and
Tang
,
B.
,
2021
, “
Multicellular LSTM-Based Deep Learning Model for Aero-Engine Remaining Useful Life Prediction
,”
Reliab. Eng. Syst. Saf.
,
216
, p.
107927
.10.1016/j.ress.2021.107927
46.
Zhang
,
J.
,
Wang
,
P.
,
Yan
,
R.
, and
Gao
,
R. X.
,
2018
, “
Deep Learning for Improvement System Remaining Life Prediction
,”
Procedia CIRP
,
72
, pp.
1033
1038
.10.1016/j.procir.2018.03.262
47.
Wu
,
Y.
,
Yuan
,
M.
,
Dong
,
S.
,
Lin
,
L.
, and
Liu
,
Y.
,
2018
, “
Remaining Useful Life Estimation of Engineered Systems Using Vanilla LSTM Neural Networks
,”
Neurocomputing
,
275
, pp.
167
179
.10.1016/j.neucom.2017.05.063
48.
Zhao
,
S.
,
Zhang
,
Y.
,
Wang
,
S.
,
Zhou
,
B.
, and
Cheng
,
C.
,
2019
, “
A Recurrent Neural Network Approach for Remaining Useful Life Prediction Utilizing a Novel Trend Features Construction Method
,”
Measurement
,
146
, pp.
279
288
.10.1016/j.measurement.2019.06.004
49.
Zhang
,
A.
,
Wang
,
H.
,
Li
,
S.
,
Cui
,
Y.
,
Liu
,
Z.
,
Yang
,
G.
, and
Hu
,
J.
,
2018
, “
Transfer Learning With Deep Recurrent Neural Networks for Remaining Useful Life Estimation
,”
Appl. Sci.
,
8
(
12
), p.
2416
.10.3390/app8122416
50.
Losi
,
E.
,
Venturini
,
M.
,
Manservigi
,
L.
, and
Bechini
,
G.
,
2022
, “
Ensemble Learning Approach to Predict Gas Turbine Trip
,”
ASME
Paper No. GT2022-80372. 10.1115/GT2022-80372
51.
Bechini
,
G.
,
Losi
,
E.
,
Manservigi
,
L.
,
Pagliarini
,
G.
,
Sciavicco
,
G.
,
Stan
,
E. I.
, and
Venturini
,
M.
,
2022
, “
Temporal Random Forest Applied to Gas Turbine Trip Prediction
,”
ASME
Paper No. GT2022-82915.10.1115/GT2022-82915
52.
Killick
,
R.
,
Fearnhead
,
P.
, and
Eckley
,
I. A.
,
2012
, “
Optimal Detection of Changepoints With a Linear Computational Cost
,”
J. Am. Stat. Assoc.
,
107
(
500
), pp.
1590
1598
.10.1080/01621459.2012.737745
53.
Kaufman
,
L.
, and
Rousseeuw
,
P. J.
,
1990
,
Finding Groups in Data: An Introduction to Cluster Analysis
,
Wiley
,
Hoboken, NJ
.
54.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-Term Memory
,”
Neural Comput.
,
9
(
8
), pp.
1735
1780
.10.1162/neco.1997.9.8.1735
55.
Srivastava
,
N.
,
Hinton
,
G.
,
Krizhevsky
,
A.
,
Sutskever
,
I.
, and
Salakhutdinov
,
R.
,
2014
, “
Dropout: A Simple Way to Prevent Neural Networks From Overfitting
,”
J. Mach. Learn. Res.
,
15
, pp.
1929
1958
.http://jmlr.org/papers/v15/srivastava14a.html
56.
Diederik
,
K.
, and
Ba
,
J.
,
2015
, “
Adam: A Method for Stochastic Optimization
,” e-print
arXiv:1412.6980
.10.48550/arXiv.1412.6980
57.
Bishop
,
C. M.
,
2006
,
Pattern Recognition and Machine Learning
,
Springer
,
New York
.
58.
Murphy
,
K. P.
,
2012
,
Machine Learning: A Probabilistic Perspective
,
The MIT Press
,
Cambridge, MA
.
59.
Wang
,
J.
,
2012
, “
Classical Multidimensional Scaling
,”
Geometric Structure of High-Dimensional Data and Dimensionality Reduction
,
Springer
,
Berlin
.
60.
Breiman
,
L.
,
2001
, “
Random Forest
,”
Mach. Learn.
,
45
(
1
), pp.
5
32
.10.1023/A:1010933404324
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