Abstract
Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively and accurately perform fault diagnosis for rotating machinery, a novel feature selection method named unified discriminant manifold learning (UDML) is proposed in this research. To be specific, the local linear relationship, the distance between adjacent points, the intra-class and inter-class variance are unified in UDML. Based on these, the local structure, global information and label information of high-dimensional features are effectively preserved by UDML. Through this dimension reduction method, homogeneous features become more concentrated while heterogeneous features become more distant. Consequently, mechanical faults could be diagnosed accurately with the help of proposed UDML. More importantly, local linear embedding algorithm, locality preserving projections algorithm, and linear discriminant analysis algorithm could be regarded as a special form of UDML. Moreover, a novel weighted neighborhood graph is constructed to effectively reduce the interference of outliers and noise. The corresponding model parameters are dynamically adjusted by the gray wolf optimization algorithm to find a subspace that discovers the intrinsic manifold structure for classification tasks. Based on the above innovations, a fault diagnosis method for rotating machinery is proposed. Through experimental verifications and comparisons with several classical feature selection algorithms, rotating machinery fault diagnosis can be more accurately performed by the proposed method.
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References
Alavi, H., Ohadi, A., & Niaki, S. T. (2022). A novel targeted method of informative frequency band selection based on lagged information for diagnosis of gearbox single and compound faults. Mechanical Systems and Signal Processing, 170, 108828. https://doi.org/10.1016/j.ymssp.2022.108828
Al-Bugharbee, H., & Trendafilova, I. (2016). A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling. Journal of Sound and Vibration, 369, 246–265. https://doi.org/10.1016/j.jsv.2015.12.052
Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40, 100378. https://doi.org/10.1016/j.cosrev.2021.100378
Bustillo, A., Reis, R., Machado, A. R., & Pimenov, D. (2022). Improving the accuracy of machine-learning models with data from machine test repetitions. Journal of Intelligent Manufacturing, 33, 203–221. https://doi.org/10.1007/s10845-020-01661-3
Cao, P., Zhang, S., & Tang, J. (2018). Gear fault data. Figshare. https://doi.org/10.6084/m9.figshare.6127874.v1
Ding, H., Gao, R. X., Isaksson, A. J., Landers, R. G., Parisini, T., & Yuan, Y. (2020). State of AI-based monitoring in smart manufacturing and introduction to focused section. IEEE/ASME Transactions on Mechatronics, 25(5), 2143–2154. https://doi.org/10.1109/TMECH.2020.3022983
Ding, X., & He, Q. (2016). Time–frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction. Mechanical Systems and Signal Processing, 80, 392–413. https://doi.org/10.1016/j.ymssp.2016.04.024
Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010. https://doi.org/10.1109/TSP.2013.2265222
He, X., Yan, S., Hu, Y., Niyogi, P., & Zhang, H. (2005). Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3), 328–340. https://doi.org/10.1109/TPAMI.2005.55
Hoang, D. T., & Kang, H. J. (2020). A motor current signal-based bearing fault diagnosis using deep learning and information fusion. IEEE Transactions on Instrumentation and Measurement, 69(6), 3325–3333. https://doi.org/10.1109/TIM.2019.2933119
Kumar, A., & Kumar, R. (2016). Manifold learning using linear local tangent space alignment (LLTSA) algorithm for noise removal in wavelet filtered vibration signal. Journal of Nondestructive Evaluation, 35, 50. https://doi.org/10.1007/s10921-016-0366-4
Lee, W. J., Mendis, G. P., Triebe, M. J., & Sutherland, J. W. (2020). Monitoring of a machining process using kernel principal component analysis and kernel density estimation. Journal of Intelligent Manufacturing, 31, 1175–1189. https://doi.org/10.1007/s10845-019-01504-w
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). 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. In 3rd European Conference of the Prognostics and Health Management Society (pp. 1–17).
Li, B., & Zhang, Y. (2011). Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis. Mechanical Systems and Signal Processing, 25(8), 3125–3134. https://doi.org/10.1016/j.ymssp.2011.05.001
Li, B., Zheng, C., & Huang, D. (2008). Locally linear discriminant embedding: An efficient method for face recognition. Pattern Recognition, 41(12), 3813–3821. https://doi.org/10.1016/j.patcog.2008.05.027
Li, F., Wang, J., Chyu, M. K., & Tang, B. (2015). Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant Analysis. Neurocomputing, 168, 505–519. https://doi.org/10.1016/j.neucom.2015.05.076
Li, H., Jiang, T., & Zhang, K. (2006). Efficient and robust feature extraction by maximum margin criterion. IEEE Transactions on Neural Networks, 17(1), 157–165. https://doi.org/10.1109/TNN.2005.860852
Li, W., Peng, M., Liu, Y., Jiang, N., Wang, H., & Duan, Z. (2018). Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method. Annals of Nuclear Energy, 113, 105–117. https://doi.org/10.1016/j.anucene.2017.11.009
Li, Y., Wang, S., Li, N., & Deng, Z. (2022). Multiscale symbolic diversity entropy: a novel measurement approach for time-series analysis and its application in fault diagnosis of planetary gearboxes. IEEE Transactions on Industrial Informatics, 18(2), 1121–1131. https://doi.org/10.1109/TII.2021.3082517
Liu, Y., Hu, Z., & Zhang, Y. (2021). Bearing feature extraction using multi-structure locally linear embedding. Neurocomputing, 428, 280–290. https://doi.org/10.1016/j.neucom.2020.11.048
Ma, S., Chu, F., & Han, Q. (2019). Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions. Mechanical Systems and Signal Processing, 127, 190–201. https://doi.org/10.1016/j.ymssp.2019.02.055
Medina, R., Macancela, J. C., Lucero, P., Cabrera, D., Sánchez, R., & Cerrada, M. (2022). Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM. Journal of Intelligent Manufacturing, 33, 1031–1055. https://doi.org/10.1007/s10845-020-01712-9
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Moehrmann, J., Burkovski, A., Baranovskiy, E., Heinze, GA., Rapoport, A., Heidemann, G. (2011). A discussion on visual interactive data exploration using self-organizing maps. In Laaksonen, J., Honkela, T. (Eds.), Advances in self-organizing maps (pp. 178–187). Springer, Berlin. https://doi.org/10.1007/978-3-642-21566-7_18.
Nieh, E. H., Schottdorf, M., Freeman, N. W., Low, R. J., Lewallen, S., Koay, S. A., Pinto, L., Gauthier, J. L., Brody, C. D., & Tank, D. W. (2021). Geometry of abstract learned knowledge in the hippocampus. Nature, 595(7865), 80–84. https://doi.org/10.1038/s41586-021-03652-7
Pang, S., Yang, X., Zhang, X., & Lin, X. (2020). Fault diagnosis of rotating machinery with ensemble kernel extreme learning machine based on fused multi-domain features. ISA Transactions, 98, 320–337. https://doi.org/10.1016/j.isatra.2019.08.053
Sha, F., & Saul, L. K. (2005). Analysis and extension of spectral methods for nonlinear dimensionality reduction. In ICML 2005- Proceedings of the 22nd International Conference on Machine Learning (pp. 784–791). https://doi.org/10.1145/1102351.1102450
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446–2455. https://doi.org/10.1109/TII.2018.2864759
Shikkenawis, G., & Mitra, S. K. (2016). On some variants of locality preserving projection. Neurocomputing, 173, 196–211. https://doi.org/10.1016/j.neucom.2015.01.100
Siblini, W., Kuntz, P., & Meyer, F. (2021). A review on dimensionality reduction for multi-label classification. IEEE Transactions on Knowledge and Data Engineering, 33(3), 839–857. https://doi.org/10.1109/TKDE.2019.2940014
Su, Z., Tang, B., Liu, Z., & Qin, Y. (2015). Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing, 157, 208–222. https://doi.org/10.1016/j.neucom.2015.01.016
Sun, C., Wang, P., Yan, R., Gao, R. X., & Chen, X. (2019). Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization. Mechanical Systems and Signal Processing, 114, 25–34. https://doi.org/10.1016/j.ymssp.2018.04.044
Tong, C., Shi, X., & Lan, T. (2016). Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis. ISA Transactions, 65, 407–417. https://doi.org/10.1016/j.isatra.2016.06.017
Unver, H. O., & Sener, B. (2021). A novel transfer learning framework for chatter detection using convolutional neural networks. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01839-3
Wang, R., Chen, H., Guan, C., Gong, W., & Zhang, Z. (2021). Research on the fault monitoring method of marine diesel engines based on the manifold learning and isolation forest. Applied Ocean Research, 112, 102681. https://doi.org/10.1016/j.apor.2021.102681
Xi, W., Li, Z., Tian, Z., & Duan, Z. (2018). A feature extraction and visualization method for fault detection of marine diesel engines. Measurement, 116, 429–437. https://doi.org/10.1016/j.measurement.2017.11.035
Xu, X., Ding, J., Liu, Q., & Chai, T. (2021). A novel multi manifold joint projections model for multimode process monitoring. IEEE Transactions on Industrial Informatics, 17(9), 5961–5970. https://doi.org/10.1109/TII.2020.3036676
Yang, A., Wang, Y., Zi, Y., & Chow, T. W. S. (2019). An enhanced trace ratio linear discriminant analysis for fault diagnosis: an illustrated example using HDD data. IEEE Transactions on Instrumentation and Measurement, 68(12), 4629–4639. https://doi.org/10.1109/TIM.2019.2900885
Zhang, Y., Peng, L., Li, X., & Xie, Y. (2020). A sparse robust adaptive filtering algorithm based on the q-Rényi Kernel function. IEEE Signal Processing Letters, 27, 476–480. https://doi.org/10.1109/LSP.2020.2978408
Zhao, X., & Jia, M. (2018). Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis. Neurocomputing, 315, 447–464. https://doi.org/10.1016/j.neucom.2018.07.038
Zhu, X., Zhang, S., Hu, R., Zhu, Y., & Song, J. (2018). Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Transactions on Knowledge and Data Engineering, 30(3), 517–529. https://doi.org/10.1109/TKDE.2017.2763618
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (nos. 51705275, 51335006, 11872222), the Fundamental Research Funds of Shandong University (nos. 2019GN046), the Key Laboratory of High-efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Shandong University Youth Interdisciplinary Science Innovation Group (nos. 2020QNQT002) and Shandong Key Laboratory of Brain Function Remodeling Open Research Program (nos. 2021NGN003). Finally, the authors are very grateful to the anonymous reviewers for their helpful comments and constructive suggestions.
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All authors contributed to the study conception and design, data collection and analysis were performed by CY, SM and QH. The first draft of the manuscript was written by CY and SM. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Yang, C., Ma, S. & Han, Q. Unified discriminant manifold learning for rotating machinery fault diagnosis. J Intell Manuf 34, 3483–3494 (2023). https://doi.org/10.1007/s10845-022-02011-1
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DOI: https://doi.org/10.1007/s10845-022-02011-1