Interpretable Network Construction for Intelligent Monitoring and Diagnosis,and Application in Inter-shaft Bearing Diagnosis While Aero-engine Test

doi:10.3901/JME.2024.12.090

Abstract

Abstract: Engine health management is the key technology to improve the safety, reliability and economic affordability of aero-engine. The intelligent diagnosis method based on neural networks has achieved great success in mechanical fault diagnosis, but the current network lacks the targeted design of aero-engine due to its“black box” nature, and has not been confirmed in engineering practice. In view of these problems, this paper proposes an interpretable network construction framework for intelligent diagnosis of aero-engine and verifies it in the real engine test data. The prior information of aero-engine vibration signals is integrated into the sparse representation model, and the iterative solution algorithm of the model is unrolled to obtain an interpretable core network architecture. The interpretable sub-network via adversarial training is constructed for detection tasks, and the interpretable deep feature extraction sub-network is constructed for intelligent fault diagnosis tasks. Therefore, the network architecture proposed in this paper has a clear theoretical basis, that is, ad-hoc interpretability. In addition, a visualization method is proposed to check whether the network has learned meaningful features, making it post-hoc interpretable. The characteristics of both ad-hoc and post-hoc interpretability make the network more credible when applied to aero-engine anomaly detection and fault diagnosis. Finally, in the long-term test data analysis of a real aero-engine, the interpretable network construction proposed in this paper provides an effective and credible results for fault diagnosis of inter-shaft bearings.

Key words: aero-engine prognostic and health management, algorithm unrolling, interpretable neural networks, anomaly detection, fault diagnosis

CLC Number:

TH17

WANG Shibin, WANG Shiao, CHEN Xuefeng, HUANG Hai, AN Botao, ZHAO Zhibin, LIU Yongquan, LI Yinghong. Interpretable Network Construction for Intelligent Monitoring and Diagnosis,and Application in Inter-shaft Bearing Diagnosis While Aero-engine Test[J]. Journal of Mechanical Engineering, 2024, 60(12): 90-106.

References

[1] 向巧，张铀，许亚平. 三型涡扇发动机故障模式与机理分析及预防技术[M]. 北京：航空工业出版社，2016. XIANG Qiao，ZHANG Zhou，XU Yaping. Failure mode and mechanism analysis and prevention technology in Type III turbofan engine[M]. Beijing：Aviation Industry Press，2016.
[2] 陈雪峰，王诗彬，曹明. 航空发动机快变信号分析及故障诊断系统[M]. 北京：科学出版社，2022. CHEN Xuefeng，WANG Shibin，CAO Ming. Aero-engine rapid change signal analysis and fault diagnosis system[M]. Beijing：Science Press，2022.
[3] KIM B，KHANNA R，KOYEJO O O. Examples are not enough，learn to criticize! criticism for interpretability[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems，2016：2288-2296.
[4] DOSHI-VELEZ F，KIM B. Towards a rigorous science of interpretable machine learning[J/OL]. [2024-03-12]. https://arxiv.org/abs/1702.08608.
[5] MILLER T. Explanation in artificial intelligence：Insights from the social sciences[J]. Artificial Intelligence，2019，267：1-38.
[6] LIPTON Z C. The mythos of model interpretability：In machine learning，the concept of interpretability is both important and slippery[J]. Queue，2018，16(3)：31-57.
[7] HOU B J，ZHOU Z H. Learning with interpretable structure from gated RNN[J]. IEEE Transactions on Neural Networks and Learning Systems，2020，31(7)：2267-2279.
[8] 杨强，范力欣，朱军. 可解释人工智能导论[M]. 北京：电子工业出版社，2022. YANG Qiang，FAN Linxin，ZHU Jun. Introduction to explain artificial intelligence[M]. Beijing：Electronic Industry Press，2022.
[9] ZHANG Q，WU Y N，ZHU S C. Interpretable convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8827-8836.
[10] SCETBON M，ELAD M，MILANFAR P. Deep k-svd denoising[J]. IEEE Transactions on Image Processing，2021，30：5944-5955.
[11] GUNNING D，STEFIK M，CHOI J，et al. XAI—Explainable artificial intelligence[J]. Science Robotics，2019，4(37)：7120.
[12] 张钹. 展望人工智能的未来[J]. 科学世界，2020(4)：85. ZHANG Bo. Prospect the future of artificial intelligence[J]. Scientific World，2020(4)：85.
[13] DU M，LIU N，HU X. Techniques for interpretable machine learning[J]. Communications of the ACM，2019，63(1)：68-77.
[14] CHEN X，DUAN Y，HOUTHOOFT R，et al. Infogan：Interpretable representation learning by information maximizing generative adversarial nets[C]// Advances in Neural Information Processing Systems，2016：29.
[15] ZEILER M D，FERGUS R. Visualizing and understanding convolutional networks[C]//Computer Vision–ECCV 2014：13th European Conference，Zurich，Switzerland，September 6-12，2014，Proceedings，Part I 13. Springer International Publishing，2014：818-833.
[16] ZHANG Q，ZHU S C. Visual interpretability for deep learning：A survey[J]. Frontiers of Information Technology & Electronic Engineering，2018，19(1)：27-39.
[17] BACH S，BINDER A，MONTAVON G，et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation[J]. PloS One，2015，10(7)：0130140.
[18] ZHOU B，KHOSLA A，LAPEDRIZA A，et al. Learning deep features for discriminative localization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：2921-2929.
[19] SHRIKUMAR A，GREENSIDE P，KUNDAJE A. Learning important features through propagating activation differences[C]//International Conference On Machine Learning，PMLR，2017：3145-3153.
[20] RIBEIRO M T，SINGH S，GUESTRIN C. " Why should i trust you?" Explaining the predictions of any classifier[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2016：1135-1144.
[21] LUNDBERG S M，LEE S I. A unified approach to interpreting model predictions[C]// Advances in Neural Information Processing Systems，2017：30.
[22] MONGA V，LI Y，ELDAR Y C. Algorithm unrolling：Interpretable，efficient deep learning for signal and image processing[J]. IEEE Signal Processing Magazine，2021，38(2)：18-44.
[23] WRIGHT J，MA Y，MAIRAL J，et al. Sparse representation for computer vision and pattern recognition[J]. Proceedings of the IEEE，2010，98(6)：1031-1044.
[24] GREGOR K，LECUN Y. Learning fast approximations of sparse coding[C]//Proceedings of the 27th International Conference on International Conference on Machine Learning，2010：399-406.
[25] SUN J，LI H，XU Z. Deep ADMM-Net for compressive sensing MRI[C]//Advances in Neural Information Processing Systems，2016：29.
[26] LEFKIMMIATIS S. Universal denoising networks：a novel CNN architecture for image denoising[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：3204-3213.
[27] DARDIKMAN-YOFFE G，ELDAR Y C. Learned SPARCOM：Unfolded deep super-resolution microscopy[J]. Optics Express，2020，28(19)：27736-27763.
[28] PAPYAN V，ROMANO Y，ELAD M. Convolutional neural networks analyzed via convolutional sparse coding[J]. The Journal of Machine Learning Research，2017，18(1)：2887-2938.
[29] SULAM J，ABERDAM A，BECK A，et al. On multi-layer basis pursuit，efficient algorithms and convolutional neural networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，42(8)：1968-1980.
[30] BECK A. First-order methods in optimization[M]. Philadelphia：Society for Industrial and Applied Mathematics，2017.
[31] PARIKH N，BOYD S. Proximal algorithms[EB/OL]. https://stanford.edu/~boyd/papers/pdf/prox_slides.pdf.
[32] MOHRI M，ROSTAMIZADEH A，TALWALKAR A. Foundations of machine learning[M]. Massachusetts：MIT Press，2018.
[33] AKCAY S，ATAPOUR-ABARGHOUEI A，BRECKON T P. Ganomaly：Semi-supervised anomaly detection via adversarial training[C]//Computer Vision–ACCV 2018：14th Asian Conference on Computer Vision，Perth，Australia，December 2-6，2018，Revised Selected Papers，Part III 14. Springer International Publishing，2019：622-637.
[34] 许鹭. 航空发动机中介轴承故障综合诊断技术研究[D]. 沈阳：沈阳航空航天大学，2018. XU Lu. Research on comprehensive fault diagnosis technology of aero-engine intermediate bearing[D]. Shenyang：Shenyang Aerospace University，2018.
[35] RAVANELLI M，BENGIO Y. Interpretable convolutional filters with sincnet[J/OL]. [2024-03-12]. https：//arxiv.org/ abs/1811.09725.