Fault classification of the rolling bearing based on minimax entropy domain adaption augmented with signal generation algorithm
Email:
lichnt@utc.edu.vn
Từ khóa:
Fast Fourier transform, bearing faults, deep learning, semi-supervised domain adaptation.
Tóm tắt
Rolling bearing faults have been capturing substantial research attention as they are the root causes of malfunctions in mechatronics systems than any other factors. The detection of rolling bearing faults in the early stage is therefore a mandatory requirement demanded by reliable industrial plants. To release the dependence of diagnostic methods on human expertise and system’s understanding, this work proposes a fault classification method for rolling bearings that is based on a deep learning framework. The framework consists of a minimax entropy domain adaptation algorithm augmented with a signal generalization algorithm. The function of the signal generalization algorithm is to reduce the domain shift between training and testing datasets that are often obtained experimentally from different working conditions. The generalized signal is then represented in the form of Fourier series whose coefficients contain intrinsic information that associated with different types of bearing faults. A convolutional neural network extracts the hidden information of bearing faults buried in the Fourier coefficients and then categorises the working condition of the bearing under test. By combining the advantages of both signal processing techniques in the frequency domain and the minimax entropy domain adaptation, the novel diagnostic framework is able to detect bearing faults from different working conditions. The effectiveness of the proposed diagnostic algorithm is experimentally verified by two case studies that were prepared with different types and levels of bearing faults.Tài liệu tham khảo
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[2]. F. Immovilli, M. Cocconcelli, A. Bellini, R. Rubini, Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals, IEEE Trans. Ind. Electron., 56 (2009) 4710–4717. https://doi.org/10.1109/TIE.2009.2025288
[3]. T. Wang, Z. Liu, G. Lu, J. Liu, Temporal-Spatio Graph Based Spectrum Analysis for Bearing Fault Detection and Diagnosis, IEEE Trans. Ind. Electron., 68 (2021) 2598–2607. https://doi.org/10.1109/TIE.2020.2975499
[4]. M. Kuncan, An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA, IEEE Access, 8 (2020) 137517–137529. https://doi.org/10.1109/ACCESS.2020.3011980
[5]. D. Huang, J. Yang, D. Zhou, G. Litak, Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index, IEEE Trans. Instrum. Meas., 69 (2020) 109–121. https://doi.org/10.1109/TIM.2019.2890933
[6]. S. Nath, J. Wu, Y. Zhao, W. Qiao, Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current, IEEE Access, 8 (2020) 44163–44174. https://doi.org/10.1109/ACCESS.2020.2977632
[7]. X. Dai, Z. Gao, From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis, IEEE Trans. Ind. Inform., 9 (2013) 2226–2238. https://doi.org/10.1109/TII.2013.2243743
[8]. S. Zhang et al., Model-Based Analysis and Quantification of Bearing Faults in Induction Machines, IEEE Trans. Ind. Appl., 56 (2020) 2158–2170. https://doi.org/10.1109/TIA.2020.2979383
[9]. M. Ojaghi, M. Sabouri, and J. Faiz, Analytic Model for Induction Motors Under Localized Bearing Faults, IEEE Trans. Energy Convers., 33 (2018) 617–626. https://doi.org/10.1109/TEC.2017.2758382
[10]. D. T. Hoang, H. J. Kang, A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion, IEEE Trans. Instrum. Meas., 69 (2020) 3325–3333. https://doi.org/10.1109/TIM.2019.2933119
[11]. F. Dalvand, S. Dalvand, F. Sharafi, M. Pecht, Current Noise Cancellation for Bearing Fault Diagnosis Using Time Shifting, IEEE Trans. Ind. Electron., 64 (2017) 8138–8147. https://doi.org/10.1109/TIE.2017.2694397
[12]. E. Elbouchikhi, V. Choqueuse, F. Auger, M. E. H. Benbouzid, Motor Current Signal Analysis Based on a Matched Subspace Detector, IEEE Trans. Instrum. Meas., 66 (2017) 3260–3270. https://doi.org/10.1109/TIM.2017.2749858
[13]. A. Naha, A. K. Samanta, A. Routray, A. K. Deb, Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length, IEEE Trans. Instrum. Meas., 66 (2017) 3249–3259. https://doi.org/10.1109/TIM.2017.2737879
[14]. L. Wen, X. Li, L. Gao, Y. Zhang, A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Trans. Ind. Electron., 65 (2018) 5990–5998. https://doi.org/10.1109/TIE.2017.2774777
[15]. P. Cao, S. Zhang, J. Tang, Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning, IEEE Access, 6 (2018) 26241–26253. https://doi.org/10.1109/ACCESS.2018.2837621
[16]. T. Han, C. Liu, W. Yang, D. Jiang, Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application, ISA Trans., 97 (2020) 269–281. https://doi.org/10.1016/j.isatra.2019.08.012
[17]. R. Zhang, H. Tao, L. Wu, Y. Guan, Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions, IEEE Access, 5 (2017) 14347–14357. https://doi.org/10.1109/ACCESS.2017.2720965
[18]. G. Xu, M. Liu, Z. Jiang, W. Shen, C. Huang, Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks, IEEE Trans. Instrum. Meas., 69 (2020) 509–520. https://doi.org/10.1109/TIM.2019.2902003
[19]. M. Ragab et al., Adversarial Multiple-Target Domain Adaptation for Fault Classification, IEEE Trans. Instrum. Meas., 70 (2021) 1–11. https://doi.org/10.1109/TIM.2020.3009341
[20]. K. Saito, D. Kim, S. Sclaroff, T. Darrell, K. Saenko, Semi-supervised Domain Adaptation via Minimax Entropy, ArXiv190406487 Cs, (2019), Accessed: Feb. 26, 2022. [Online]. Available: http://arxiv.org/abs/1904.06487
[21]. J. R. Stack, T. G. Habetler, R. G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, IEEE Trans. Ind. Appl., 40 (2004) 735–739. https://doi.org/10.1109/TIA.2004.827454
[22]. S. A. McInerny, Y. Dai, Basic vibration signal processing for bearing fault detection, IEEE Trans. Educ., 46 (2003) 149–156. https://doi.org/10.1109/TE.2002.808234
[23]. C. Lessmeier, J. Kimotho, D. Zimmer, W. Sextro, Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification, Jul. 2016.
[24]. F. B. Oswald, E. V. Zaretsky, J. V. Poplawski, Effect of Internal Clearance on Load Distribution and Life of Radially Loaded Ball and Roller Bearings, Tribol. Trans., 55 (2012) 245–265. https://doi.org/10.1080/10402004.2011.639050
[25]. E. T. S. Calvo, Diagnostics of Rotor Asymmetries in Inverter-Fed, Variable Speed Induction Machine. University of Siegen: Logos Verlag Berlin GmbH, (2009).
[26]. M. Long, Z. Cao, J. Wang, M. I. Jordan, Conditional Adversarial Domain Adaptation, ArXiv170510667 Cs, (2018), Accessed: Apr. 21, 2022. [Online]. Available: http://arxiv.org/abs/1705.10667
[27]. Y. Ganin et al., Domain-Adversarial Training of Neural Networks, ArXiv150507818 Cs Stat, (2016), Accessed: Apr. 21, 2022. [Online]. Available: http://arxiv.org/abs/1505.07818
[28]. M. Long, Y. Cao, J. Wang, M. I. Jordan, Learning Transferable Features with Deep Adaptation Networks, ArXiv150202791 Cs, (2015), Accessed: Apr. 30, 2022. [Online]. Available: http://arxiv.org/abs/1502.02791
[29]. W. A. Smith, R. B. Randall, Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study, Mech. Syst. Signal Process., 64–65 (2015) 100–131. https://doi.org/10.1016/j.ymssp.2015.04.021
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Kiểu trích dẫn
Van Trang, P., & Thanh Lich, N. (1705251600). Fault classification of the rolling bearing based on minimax entropy domain adaption augmented with signal generation algorithm. Tạp Chí Khoa Học Giao Thông Vận Tải, 75(1), 1110-1124. https://doi.org/10.47869/tcsj.75.1.1
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