Article
A Weakly Supervised Gas-Path Anomaly Detection Method for
Civil Aero-Engines Based on Mapping Relationship Mining of
Gas-Path Parameters and Improved Density Peak Clustering
Hao Sun
1
, Xuyun Fu
1,
* and Shisheng Zhong
2
Citation: Sun, H.; Fu, X.; Zhong, S. A
Weakly Supervised Gas-Path
Anomaly Detection Method for Civil
Aero-Engines Based on Mapping
Relationship Mining of Gas-Path
Parameters and Improved Density
Peak Clustering. Sensors 2021, 21,
4526. https://doi.org/10.3390/
s21134526
Academic Editors: Kim Phuc Tran,
Athanasios Rakitzis and Khanh T.
P. Nguyen
Received: 24 May 2021
Accepted: 25 June 2021
Published: 1 July 2021
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4.0/).
1
Department of Mechanical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China;
19s130232@stu.hit.edu.cn
2
Department of Mechanical Engineering, Harbin Institute of Technology, Harbin 150000, China;
zhongss@hit.edu.cn
* Correspondence: fuxuyun@hit.edu.cn; Tel.: +86-180-6310-2699
Abstract:
Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is
essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised
gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of
gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder,
composed of an attention mechanism and a long short-term memory neural network, is used to
construct the mapping relationship mining model among gas-path parameters. The predicted
values of gas-path parameters under the restriction of mapping relationships are obtained. The
deviation degree from the original values to the predicted values is regarded as the feature. To
force the extracted features to better reflect the anomalies and make full use of weakly supervised
labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed.
This loss function can be combined with a simple classifier to significantly improve the feature
extraction results, in which anomaly samples are more different from normal samples and do not
reduce the mining precision. Finally, an anomaly detection method is deployed based on improved
density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the
improved density peak clustering method, the local density is enhanced by K-nearest neighbors,
and the clustering effect is improved by a new outlier threshold determination method and a new
outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering
can be improved, and the influence of outliers on the clustering process reduced. By introducing
weakly supervised label information and automatically iterating according to clustering and anomaly
detection results to update the hyperparameter settings, a weakly supervised anomaly detection
method without complex parameter adjustment processes can be implemented. The experimental
results demonstrate the superiority of the proposed method.
Keywords: civil aero-engine; anomaly detection; weakly supervised; mapping relationship mining;
improved density peak clustering
1. Introduction
The civil aero-engine is the heart of aircraft, and it is highly reliable and has a low
anomaly rate. However, the loss caused by civil aero-engine anomalies is unacceptable.
According to incomplete statistics, more than 90% of civil aero-engine anomalies are related
to gas paths. The cost of handling these anomalies accounts for 60% of the total maintenance
cost of civil aircraft, and the cost of each repair can be as high as 2–3 million US dollars [
1
].
If an early intervention for gas-path anomalies can be made, passenger safety and the
economic benefit of the airline can be guaranteed to the maximum extent. Therefore, a
high-precision gas-path anomaly detection method is critical.
Sensors 2021, 21, 4526. https://doi.org/10.3390/s21134526 https://www.mdpi.com/journal/sensors
