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Discover unknown fault categories through active query evidence model

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Abstract

Intelligent fault diagnosis plays an important role in machine health management. Fault data in real applications are usually imbalanced, which makes some categories unknown to the learner. Some recent approaches assign an unknown label to all these categories, which may not be sufficient for further remending operations. This paper proposes unknown fault category exploration through active query evidence model (UFAE), which effectively distinguishes unknown faults, and balances the query to various unknown sub-faults, thereby providing better fault identification. First, we introduce subjective logic to model data evidence, to effectively explore instances with unknown faults. Second, we propose an active query strategy to select unknown subfaults. This process is executed iteratively to explore as many unknown sub-faults as possible, and eventually produces a high quality learner. We conducted experiments on classic fault datasets, including bearing structured data and pumping unit image data. The experimental results showed that UFAE achieved effective unknown sub-fault identification and outperformed competitors in open fault identification. Additionally, UFAE was more robust to unknown faults.

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References

  1. Li, S, Cui, Z, Ye, X, Feng, J, Yang, Y, He, Z (2020). Chip-Based Microwave-Photonic Radar for High-Resolution Imaging. Laser & Photonics Reviews, 14(10). http://ides.nuaa.edu.cn

  2. Asad B, Vaimann T, Belahcen A, Kallaste A, Rassõlkin A, Iqbal MN (2020) The cluster computation-based hybrid fem-analytical model of induction motor for fault diagnostics. Applied Sciences 10(21). https://doi.org/10.3390/app10217572. https://www.mdpi.com/2076-3417/10/21/7572

  3. Bendale A, Boult TE (2016) Towards open set deep networks. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 1563–1572. IEEE Computer Society

  4. Bosman AS, Engelbrecht A, Helbig M (2020) Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions. Neurocomputing 400:113–136. https://doi.org/10.1016/j.neucom.2020.02.113. https://www.sciencedirect.com/science/article/pii/S0925231220303593

  5. Boztas G, Tuncer T (2022) A fault classification method using dynamic centered one-dimensional local angular binary pattern for a pmsm and drive system. Neural Comput Appl 34(3):1981–1992. https://doi.org/10.1007/s00521-021-06534-1

    Article  Google Scholar 

  6. Chang N, Yu ZD, Wang YX, Anandkumar A, Fidler S, Alvarez JM (2021) Image-level or object-level? A tale of two resampling strategies for long-tailed detection. In: Proc Int Conf Mach Learn (ICML), pp 1463–1472

  7. Chen M, Zhu K, Wang R, Niyato D (2020) Active learning-based fault diagnosis in self-organizing cellular networks. IEEE Commun Lett 24(8):1734–1737. https://doi.org/10.1109/LCOMM.2020.2991449

    Article  Google Scholar 

  8. Cheng DD, Liu LJ, Yu Z (2021) Cnn-based intelligent fault-tolerant control design for turbofan engines with actuator faults. IEEE Access 9:28122–28139

    Article  Google Scholar 

  9. Chu P, Bian X, Liu SP, Ling HB (2020) Feature space augmentation for long-tailed data. In: Proc Eur Conf Comput Vis (ECCV), pp 694–710

  10. De Almeida LF, Bizarria JW, Bizarria FC, Mathias MH (2015) Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron. J Vib Control 21(16):3456–3464

    Article  Google Scholar 

  11. Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Annals Math Stat 38(2):325–339

    Article  MathSciNet  MATH  Google Scholar 

  12. Du WL, Tao JF, Li YM, Liu CL (2014) Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Proc 43(1–2):57–75

    Article  Google Scholar 

  13. Gal Y, Ghahramani Z (2016) Bayesian convolutional neural networks with bernoulli approximate variational inference. Proc Int Conf Learn Represent (ICLR)

  14. Han ZY, Wang J (2018) A fault diagnosis method based on active example selection. J Circuits Syst Comput 27(01):1850013

    Article  Google Scholar 

  15. He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 770–778

  16. He Y, Song KC, Meng QG, Yan YH (2019) An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Meas 69(4):1493–1504

    Article  Google Scholar 

  17. Hong W, Cai WJ, Wang S, Mileta MT (2018) Mechanical wear debris feature, detection, and diagnosis: A review. Chin J Aeronaut 31(5):867–882

    Article  Google Scholar 

  18. Jian CX, Yang KJ, Ao YH (2021) Industrial fault diagnosis based on active learning and semi-supervised learning using small training set. Eng Appl Artif Intell 104:104365. https://doi.org/10.1016/j.engappai.2021.104365

    Article  Google Scholar 

  19. Jøsang A (2016) Subjective Logic: A formalism for reasoning under uncertainty. Intell. Found. Theory Algorithms. Springer, Artif. https://doi.org/10.1007/978-3-319-42337-1

    Book  MATH  Google Scholar 

  20. Jun LW, Hui L, Sai G, Tao C (2020) Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities. Control Eng Pract 105:104637. https://doi.org/10.1016/j.conengprac.2020.104637. https://www.sciencedirect.com/science/article/pii/S0967066120302070

  21. Jung D (2020) Data-driven open-set fault classification of residual data using bayesian filtering. IEEE Trans Control Syst Technol 28(5):2045–2052

    Article  Google Scholar 

  22. Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. Proc Neural Inf Process Syst (NIPS) 28

  23. Lao ZP, He DQ, Wei ZX, Hui S, Jin ZZ, Jian M, Hui RC (2023) Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved lightgbm. Eng Failure Anal 148:107219. https://doi.org/10.1016/j.engfailanal.2023.107219. https://www.sciencedirect.com/science/article/pii/S1350630723001735

  24. Lee K, Lee H, Lee K, Shin, J (2018) Training confidence-calibrated classifiers for detecting out-of-distribution samples. In: Proc Int Conf Learn Represent (ICLR)

  25. Liu XM, Ming XN (2019) A nonlinear grey forecasting model with double shape parameters and its application. Appl Math Comput 360:203–212. https://doi.org/10.1016/j.amc.2019.05.012. https://www.sciencedirect.com/science/article/pii/S0096300319304059

  26. Lv XX, Wang HX, Zhang X, Liu YX, Jiang D, Wei B (2021) An evolutional svm method based on incremental algorithm and simulated indicator diagrams for fault diagnosis in sucker rod pumping systems. J Pet Sci Eng 203:108806. https://doi.org/10.1016/j.petrol.2021.108806

    Article  Google Scholar 

  27. Molchanov D, Ashukha A, Vetrov D (2017) Variational dropout sparsifies deep neural networks. In: Proc Int Conf Mach Learn (ICML), pp 2498–2507

  28. Mundt M, Pliushch I, Majumder S, Ramesh V (2019) Open set recognition through deep neural network uncertainty: does out-of-distribution detection require generative classifiers?. In: Proc IEEE/CVF Int Conf Comput Vis (ICCV), pp 753–757

  29. Nielsen, F (2022) The kullback–leibler divergence between lattice gaussian distributions. Journal of the Indian Institute of Science pp 1–12

  30. Riaz N, Shah SIA, Rehman F, Gilani SO, Udin E (2020) A novel 2-d current signal-based residual learning with optimized softmax to identify faults in ball screw actuators. IEEE Access 8:115299–115313. https://doi.org/10.1109/ACCESS.2020.3004489

    Article  Google Scholar 

  31. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: ECCV, pp. 213–226. Springer

  32. Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. pp 3183–3193

  33. Shafer G (1976) A mathematical theory of evidence. Princeton University Press

    Book  MATH  Google Scholar 

  34. Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mech Syst Signal Proc 64:100–131

    Article  Google Scholar 

  35. Tang YP, Huang SJ (2019) Self-paced active learning: Query the right thing at the right time. Proc. AAAI 33:5117–5124

    Article  Google Scholar 

  36. Thomas JB, Chaudhari SG, Shihabudheen KV, Verma NK (2023) Cnn-based transformer model for fault detection in power system networks. IEEE Trans Instrum Meas 72:1–10. https://doi.org/10.1109/TIM.2023.3238059

    Article  Google Scholar 

  37. Tian Y, Wang ZL, Zhang LP, Lu C, Ma J (2018) A subspace learning-based feature fusion and open-set fault diagnosis approach for machinery components. Adv Eng Inform 36:194–206

    Article  Google Scholar 

  38. Van M, Kang HJ (2015) Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE Trans Ind Informat 12(1):124–135

    Article  Google Scholar 

  39. Wang M, Fu K, Min F, Jia XY (2020) Active learning through label error statistical methods. Knowl-Based Syst 189:105140

    Article  Google Scholar 

  40. Wang M, Yang CY, Zhao F, Min F, Wang XZ (2022) Cost-sensitive active learning for incomplete data. IEEE Trans Syst Man Cybern -Syst, pp 1–12. https://doi.org/10.1109/TSMC.2022.3182122

  41. Wang M, Zhou L, Li Q, Aa Zhang (2023) Open world long-tailed data classification through active distribution optimization. Exp Syst Appl 213:119054. https://doi.org/10.1016/j.eswa.2022.119054. https://www.sciencedirect.com/science/article/pii/S0957417422020723

  42. Wang T, Zhang L, Wang X (2023) Fault detection for motor drive control system of industrial robots using cnn-lstm-based observers. CES Transactions on Electrical Machines and Systems pp. 1–9. https://doi.org/10.30941/CESTEMS.2023.00014

  43. Wang ZR, Wang J, Wang YR (2018) An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing 310:213–222. https://doi.org/10.1016/j.neucom.2018.05.024

    Article  Google Scholar 

  44. Yan WH, Wang J, Lu S, Zhou M, Peng X (2023) A review of real-time fault diagnosis methods for industrial smart manufacturing. Processes 11(2). https://doi.org/10.3390/pr11020369. https://www.mdpi.com/2227-9717/11/2/369

  45. Yang J, Yang Y, Xie G (2020) Diagnosis of incipient fault based on sliding-scale resampling strategy and improved deep autoencoder. IEEE Sensors J 20(15):8336–8348. https://doi.org/10.1109/JSEN.2020.2976523

    Article  Google Scholar 

  46. Yu XL, Zhao ZB, Zhang XW, Sun C, Zhang QY, Chen XF (2020) Coupling deep models and extreme value theory for open set fault diagnosis. In: Int Conf Sens Meas Data Anal, pp 118–123

  47. Zhang C, Guo QX, Li Y (2020) Fault detection in the tennessee eastman benchmark process using principal component difference based on k-nearest neighbors. IEEE Access 8:49999–50009

    Article  Google Scholar 

  48. Zhang C, Xu LQ, Li XW, Wang HY (2018) A method of fault diagnosis for rotary equipment based on deep learning. In: Prognostics System Health Manag. Conf, pp 958–962

  49. Zhang JQ, Sun Y, Guo L, Gao HL, Hong X, Song HL (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut 33:439–447

    Article  Google Scholar 

  50. Zhang TC, Chen JL, Li FD, Zhang KY, Lv HX, He SL, Xu EY (2022) Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA Trans 119:152–171. https://doi.org/10.1016/j.isatra.2021.02.042

  51. Zhao X, Xiao J, Yu S, Li H, Zhang B (2023) Weight-guided class complementing for long-tailed image recognition. Pattern Recognit 138:109374. https://doi.org/10.1016/j.patcog.2023.109374. https://www.sciencedirect.com/science/article/pii/S0031320323000754

  52. Zhou ZH (2022) Open-environment machine learning. Natl Sci Rev 9(8):nwac123

  53. Zou L, Li Y, Xu FY (2020) An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case. Neurocomputing 407:105–120

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62006200); the Natural Science Foundation of Sichuan Province (2022YFG0179); the Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology) (PLC20211104); Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance (2020CX020000). We thank Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

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  1. Electrical Engineering and Information Department, Southwest Petroleum University, Chengdu, 610500, China

    Min Wang, Xiaoyu Jiang & Ting Wen

  2. Baikouquan Oil Production Plant of Xinjiang Oilfield Company, Xinjiang, 834000, China

    Nengji Jiang

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  1. Min Wang
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Correspondence to Min Wang.

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Wang, M., Jiang, X., Wen, T. et al. Discover unknown fault categories through active query evidence model. Appl Intell 53, 27808–27825 (2023). https://doi.org/10.1007/s10489-023-04965-y

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