TY - JOUR
T1 - Real-time novelty detection of an industrial gas turbine using performance deviation model and extreme function theory
AU - Gu, Xiwen
AU - Yang, Shixi
AU - Sui, Yongfeng
AU - Papatheou, Evangelos
AU - Ball, Andrew D.
AU - Gu, Fengshou
N1 - Funding Information:
The authors acknowledge the support from the National Natural Science Foundation of China (Grant No. U1809219, No. 51705302) and the Key Research and Development Project of Zhejiang Province (Grant No. 2020C01088).
Publisher Copyright:
© 2021 Elsevier Ltd
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Novelty detection is crucial to ensure the availability and reliability of an industrial gas turbine. With the application of modern health monitoring systems, there is an ample amount of data gathered from gas turbines, however they are usually from normal events with limited knowledge of any novelty. In current practice, the unknown event is detected by comparing with a model of normality through pointwise approaches, which is inefficient in terms of false alarms or missing alarms. This paper proposes an accurate novelty detection approach using performance deviation model and extreme function theory. The model is established from the multi-sensor real-time performance data. Outputs of the model, that is, the deviation curves, are considered as functions instead of individual data points to test the status of the system as ‘normal’ or ‘abnormal’ by the extreme value theory. The effectiveness of the proposed approach is demonstrated by the monitoring data from a single shaft gas turbine on site. Compared with other traditional methods, the proposed approach is superior in terms of high detection accuracy and high sensitivity with a good balance between the false alarm rate and missing alarm rate. This paper provides a reliable approach for the real-time health monitoring of the industrial gas turbines.
AB - Novelty detection is crucial to ensure the availability and reliability of an industrial gas turbine. With the application of modern health monitoring systems, there is an ample amount of data gathered from gas turbines, however they are usually from normal events with limited knowledge of any novelty. In current practice, the unknown event is detected by comparing with a model of normality through pointwise approaches, which is inefficient in terms of false alarms or missing alarms. This paper proposes an accurate novelty detection approach using performance deviation model and extreme function theory. The model is established from the multi-sensor real-time performance data. Outputs of the model, that is, the deviation curves, are considered as functions instead of individual data points to test the status of the system as ‘normal’ or ‘abnormal’ by the extreme value theory. The effectiveness of the proposed approach is demonstrated by the monitoring data from a single shaft gas turbine on site. Compared with other traditional methods, the proposed approach is superior in terms of high detection accuracy and high sensitivity with a good balance between the false alarm rate and missing alarm rate. This paper provides a reliable approach for the real-time health monitoring of the industrial gas turbines.
KW - Extreme functions
KW - False alarms
KW - Industrial gas turbines
KW - Missing alarms
KW - Novelty detection
KW - Performance deviation
UR - http://www.scopus.com/inward/record.url?scp=85103776772&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2021.109339
DO - 10.1016/j.measurement.2021.109339
M3 - Article
AN - SCOPUS:85103776772
VL - 178
JO - Measurement
JF - Measurement
SN - 1536-6367
M1 - 109339
ER -