A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples

Zhu, Xinyun; Sun, Jianzhong; Hu, Hanchun; Li, Chunhua

doi:10.17531/ein/174382

Artykuł - szczegóły

Tytuł artykułu

A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples

Autorzy

Zhu Xinyun , Sun Jianzhong , Hu Hanchun , Li Chunhua

Treść / Zawartość

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DOI

10.17531/ein/174382

Warianty tytułu

Języki publikacji

Abstrakty

Health monitoring and fault detection of complex aircraft systems are paramount for ensuring reliable and efficient operation. The availability of monitoring data from modern aircraft onboard sensors provides a wealth of big data for developing deep learning-based fault detection methods. However, aircraft onboard systems typically have limited labeled fault samples and large amounts of unlabeled data. To better utilize the information contained in limited labeled fault samples, a deep learning-based semi-supervisedfault detection method is proposed, which leverages a small number of labeled fault samples to enhance its performance. A novel sample pairing strategy is introduced to improve algorithm performance by iteratively utilizing fault samples. A comprehensive loss function is employed to accurately reconstruct normal samples and effectively separate fault samples. The results of a case study using real data from a commercial aircraft fleet demonstrate the superiority of the proposed method over existing techniques, with improvements of approximately 16.7% in AP, 9.5% in AUC, and 19.2% in F1 score. Ablation studies confirm that performance can be further improved by incorporating additional labeled fault samples during training. Furthermore, the algorithm demonstrates good generalization ability.

Słowa kluczowe

fault detection semi-supervised aircraft system flight data time-series data

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2023

Tom

Vol. 25, no. 4

Strony

art. no. 174382

Opis fizyczny

Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Zhu Xinyun

zhuxinyun@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

https://orcid.org/0000-0002-1813-6710

autor

Sun Jianzhong

sunjianzhong@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

https://orcid.org/0000-0003-1806-7388

autor

Hu Hanchun

acfun0218@164.com

Nanjing University of Aeronautics and Astronautics, China

autor

Li Chunhua

nuaaccalch@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

Bibliografia

1. Angiulli F, Pizzuti C. Fast Outlier Detection in High Dimensional Spaces. In Elomaa T, Mannila H, Toivonen H (eds): Principles of Data Mining and Knowledge Discovery, Berlin, Heidelberg, Springer Berlin Heidelberg: 2002; 2431: 15–27, https://doi.org/10.1007/3-540-45681-3_2.
2. Basumallik S, Ma R, Eftekharnejad S. Packet-data anomaly detection in PMU-based state estimator using convolutional neural network. International Journal of Electrical Power & Energy Systems 2019; 107: 690–702, https://doi.org/10.1016/j.ijepes.2018.11.013.
3. Belagoune S, Bali N, Bakdi A et al. Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement 2021; 177: 109330, https://doi.org/10.1016/j.measurement.2021.109330.
4. Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 1994; 5(2): 157–166, https://doi.org/10.1109/72.279181.
5. Berlemont S, Lefebvre G, Duffner S, Garcia C. Class-balanced siamese neural networks. Neurocomputing 2018; 273: 47–56, https://doi.org/10.1016/j.neucom.2017.07.060.
6. Breunig M, Kröger P, Ng R, Sander J. LOF: Identifying Density-Based Local Outliers. 2000; 29: 104, https://doi.org/10.1145/342009.335388.
7. Canizo M, Triguero I, Conde A, Onieva E. Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study. Neurocomputing 2019; 363: 246–260, https://doi.org/10.1016/j.neucom.2019.07.034.
8. Carvalho T P, Soares F A A M N, Vita R et al. A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering 2019; 137: 106024, https://doi.org/10.1016/j.cie.2019.106024.
9. Castellani A, Schmitt S, Squartini S. Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning. IEEE Transactions on Industrial Informatics 2021; 17(7): 4733–4742, https://doi.org/10.1109/TII.2020.3019788.
10. Che C, Wang H, Fu Q, Ni X. Combining multiple deep learning algorithms for prognostic and health management of aircraft. Aerospace Science and Technology 2019; 94: 105423, https://doi.org/10.1016/j.ast.2019.105423.
11. Choi K, Yi J, Park C, Yoon S. Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines. IEEE Access 2021; 9: 120043–120065, https://doi.org/10.1109/ACCESS.2021.3107975.
12. Dong Y. Implementing Deep Learning for comprehensive aircraft icing and actuator/sensor fault detection/identification. Engineering Applications of Artificial Intelligence 2019; 83: 28–44, https://doi.org/10.1016/j.engappai.2019.04.010.
13. Dou S, Yang K, Poor H V. PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model. IEEE Internet of Things Journal 2019; 6(6): 9645–9655, https://doi.org/10.1109/JIOT.2019.2930202.
14. Ergen T, Kozat S S. Unsupervised Anomaly Detection With LSTM Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 2020; 31(8): 3127–3141, https://doi.org/10.1109/TNNLS.2019.2935975.
15. Feng Z, Tang J, Dou Y, Wu G. Learning Discriminative Features for Semi-Supervised Anomaly Detection. ICASSP 2021 -2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, IEEE: 2021: 2935–2939, https://doi.org/10.1109/ICASSP39728.2021.9414285.
16. Gers F A, Schmidhuber J, Cummins F. Learning to Forget: Continual Prediction with LSTM. Neural Computation 2000; 12(10): 2451–2471, https://doi.org/10.1162/089976600300015015.
17. Gradel S, Aigner B, Stumpf E. Model-based safety assessment for conceptual aircraft systems design. CEAS Aeronautical Journal 2022; 13(1): 281–294, https://doi.org/10.1007/s13272-021-00562-2.
18. Helbing G, Ritter M. Deep Learning for fault detection in wind turbines. Renewable and Sustainable Energy Reviews 2018; 98: 189–198, https://doi.org/10.1016/j.rser.2018.09.012.
19. Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation 1997; 9(8): 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735.
20. Hsu C-Y, Liu W-C. Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. Journal of Intelligent Manufacturing 2021; 32(3): 823–836, https://doi.org/10.1007/s10845-020-01591-0.
21. Ince T, Kiranyaz S, Eren L et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics 2016; 63(11): 7067–7075, https://doi.org/10.1109/TIE.2016.2582729.
22. Jalayer M, Orsenigo C, Vercellis C. Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms. Computers in Industry 2021; 125: 103378, https://doi.org/10.1016/j.compind.2020.103378.
23. Jeyaraj A K, Liscouët-Hanke S. A Safety-Focused System Architecting Framework for the Conceptual Design of Aircraft Systems. Aerospace 2022; 9(12): 791, https://doi.org/10.3390/aerospace9120791.
24. Jiang F, Fu Y, Gupta B B et al. Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security. IEEE Transactions on Sustainable Computing 2020; 5(2): 204–212, https://doi.org/10.1109/TSUSC.2018.2793284.
25. Jiang G, He H, Xie P, Tang Y. Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement 2017; 66(9): 2391–2402, https://doi.org/10.1109/TIM.2017.2698738.
26. Jiang M, Hou C, Zheng A et al. Weakly Supervised Anomaly Detection: A Survey. 2023. doi:10.48550/arXiv.2302.04549, https://doi.org/10.48550/arXiv.2302.04549.
27. Kłosowski G, Rymarczyk T, Niderla K et al. Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography –a hybrid approach. Eksploatacja i Niezawodność – Maintenance and Reliability 2023. doi:10.17531/ein.2023.1.11, https://doi.org/10.17531/ein.2023.1.11.
28. Li G, Jung J J. Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges. Information Fusion 2023; 91: 93–102, https://doi.org/10.1016/j.inffus.2022.10.008.
29. Li X, Li J, Qu Y, He D. Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning. Chinese Journal of Aeronautics 2020; 33(2): 418–426, https://doi.org/10.1016/j.cja.2019.04.018.
30. Liu C, Sun J, Liu H et al. Complex engineered system health indexes extraction using low frequency raw time-series data based on deep learning methods. Measurement 2020; 161: 107890, https://doi.org/10.1016/j.measurement.2020.107890.
31. Liu F T, Ting K M, Zhou Z-H. Isolation Forest. 2008 Eighth IEEE International Conference on Data Mining, 2008: 413–422, https://doi.org/10.1109/ICDM.2008.17.
32. Liu J, Song X, Zhou Y et al. Deep anomaly detection in packet payload. Neurocomputing 2022; 485: 205–218, https://doi.org/10.1016/j.neucom.2021.01.146.
33. Lopez Pinaya W H, Vieira S, Garcia-Dias R, Mechelli A. Chapter 11 -Autoencoders. In Mechelli A, Vieira S (eds): Machine Learning, Academic Press: 2020: 193–208, https://doi.org/10.1016/B978-0-12-815739-8.00011-0.
34. Mei S, Cheng J, He X et al. A Novel Weakly Supervised Ensemble Learning Framework for Automated Pixel-Wise Industry Anomaly Detection. IEEE Sensors Journal 2022; 22(2): 1560–1570, https://doi.org/10.1109/JSEN.2021.3131908.
35. Mitra S, Mukhopadhyay R, Chattopadhyay P. PSO driven designing of robust and computation efficient 1D-CNN architecture for transmission line fault detection. Expert Systems with Applications 2022; 210: 118178, https://doi.org/10.1016/j.eswa.2022.118178.
36. Moghaddam M, Chen Q, Deshmukh A V. A neuro-inspired computational model for adaptive fault diagnosis. Expert Systems with Applications 2020; 140: 112879, https://doi.org/10.1016/j.eswa.2019.112879.
37. Nguyen H D, Tran K P, Thomassey S, Hamad M. Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management 2021; 57: 102282, https://doi.org/10.1016/j.ijinfomgt.2020.102282.
38. Ning S, Sun J, Liu C, Yi Y. Applications of deep learning in big data analytics for aircraft complex system anomaly detection. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2021; 235(5): 923–940, https://doi.org/10.1177/1748006X211001979.
39. Pang G, Shen C, van den Hengel A. Deep Anomaly Detection with Deviation Networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage AK USA, ACM: 2019: 353–362, https://doi.org/10.1145/3292500.3330871.
40. Pang G, Shen C, Jin H, van den Hengel A. Deep Weakly-supervised Anomaly Detection. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, Association for Computing Machinery: 2023: 1795–1807, https://doi.org/10.1145/3580305.3599302.
41. Peterson L E. K-nearest neighbor. Scholarpedia 2009; 4(2): 1883, https://doi.org/10.4249/scholarpedia.1883.
42. Plakias S, Boutalis Y S. Fault detection and identification of rolling element bearings with Attentive Dense CNN. Neurocomputing 2020; 405: 208–217, https://doi.org/10.1016/j.neucom.2020.04.143.
43. Rai K, Hojatpanah F, Badrkhani Ajaei F, Grolinger K. Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders. Energies 2021; 14(12): 3623, https://doi.org/10.3390/en14123623.
44. RamanMR G, Somu N, Mathur A P. A multilayer perceptron model for anomaly detection in water treatment plants. International Journal of Critical Infrastructure Protection 2020; 31: 100393, https://doi.org/10.1016/j.ijcip.2020.100393.
45. Reddy K K, Sarkar S, Venugopalan V, Giering M. Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach. Annual Conference of the PHM Society 2016. doi:10.36001/phmconf.2016.v8i1.2549, https://doi.org/10.36001/phmconf.2016.v8i1.2549.
46. Saito T, Rehmsmeier M. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE 2015; 10(3): e0118432, https://doi.org/10.1371/journal.pone.0118432.
47. Schölkopf B, Platt J C, Shawe-Taylor J et al. Estimating the Support of a High-Dimensional Distribution. Neural Computation 2001; 13(7): 1443–1471, https://doi.org/10.1162/089976601750264965.
48. 48. Shen K, Zhao D. An EMD-LSTM Deep Learning Method for Aircraft Hydraulic System Fault Diagnosis under Different Environmental Noises. Aerospace 2023; 10(1): 55, https://doi.org/10.3390/aerospace10010055.
49. Shu X, Zhang S, Li Y, Chen M. An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoring. Eksploatacja i Niezawodność – Maintenance and Reliability 2022; 24(4): 758–770, https://doi.org/10.17531/ein.2022.4.16.
50. Shyu M-L, Chen S-C, Sarinnapakorn K, Chang L. Principal Component-based Anomaly Detection Scheme. In Young Lin T, Ohsuga S, Liau C-J, Hu X (eds): Foundations and Novel Approaches in Data Mining, Berlin/Heidelberg, Springer-Verlag: 2005; 9: 311–329, https://doi.org/10.1007/11539827_18.
51. Su S, Sun Y, Peng C, Wang Y. Aircraft Bleed Air System Fault Prediction based on Encoder-Decoder with Attention Mechanism. Eksploatacja i Niezawodność – Maintenance and Reliability 2023. doi:10.17531/ein/167792, https://doi.org/10.17531/ein/167792.
52. Sun J, Wang F, Ning S. Aircraft air conditioning system health state estimation and prediction for predictive maintenance. Chinese Journal of Aeronautics 2020; 33(3): 947–955, https://doi.org/10.1016/j.cja.2019.03.039.
53. Sun W, Shao S, Zhao R et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 2016; 89: 171–178, https://doi.org/10.1016/j.measurement.2016.04.007.
54. Yang K, Ren J, Zhu Y, Zhang W. Active Learning for Wireless IoT Intrusion Detection. IEEE Wireless Communications 2018; 25(6): 19–25, https://doi.org/10.1109/MWC.2017.1800079.
55. Zaheer M Z, Mahmood A, Khan M H et al. An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, IEEE: 2021: 2595–2601, https://doi.org/10.1109/ICCVW54120.2021.00293.
56. Zeiser A, Özcan B, Van Stein B, Bäck T. Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection. Computers in Industry 2023; 146: 103852, https://doi.org/10.1016/j.compind.2023.103852.
57. Zhang C, Song D, Chen Y et al. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. Proceedings of the AAAI Conference on Artificial Intelligence 2019; 33(01): 1409–1416, https://doi.org/10.1609/aaai.v33i01.33011409.
58. Zhang J, Sun Y, Guo L et al. A new bearing fault diagnosis method based on modified convolutional neural networks. Chinese Journal of Aeronautics 2020; 33(2): 439–447, https://doi.org/10.1016/j.cja.2019.07.011.
59. Zhao G, Zhang G, Ge Q, Liu X. Research advances in fault diagnosis and prognostic based on deep learning. 2016 Prognostics and System Health Management Conference (PHM-Chengdu), 2016: 1–6, https://doi.org/10.1109/PHM.2016.7819786.
60. Zhao Y, Nasrullah Z, Li Z. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of Machine Learning Research 2019; 20(96): 1–7.
61. Zhao Y, Zhao H, Ai J, Dong Y. Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors. International Journal of Aerospace Engineering 2022; 2022: 1–17, https://doi.org/10.1155/2022/2918458.
62. Zhi Z, Liu L, Liu D, Hu C. Fault Detection of the Harmonic Reducer Based on CNN-LSTM With a Novel Denoising Algorithm. IEEE Sensors Journal 2022; 22(3): 2572–2581, https://doi.org/10.1109/JSEN.2021.3137992.
63. Zhou Y, Song X, Zhang Y et al. Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems 2022; 33(6): 2454–2465, https://doi.org/10.1109/TNNLS.2021.3086137.
64. Zhou Z-H. A brief introduction to weakly supervised learning. National Science Review 2018; 5(1): 44–53, https://doi.org/10.1093/nsr/nwx106.

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