Adaptive Wavelet-Based Physics-Informed CNN for Bearing Fault Diagnosis
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Published
Apr 5, 2025
Reza Hassannejad
Mir Mohammad Ettefagh
Yousef Bahrami Mossayebi
Abstract
With the increasing expansion of data science into various fields, the application of deep neural networks in the fault diagnosis of rotating machines has attracted significant attention from researchers. However, in the methods available in the literature, the physical characteristics of the problem are not incorporated into the structure of deep networks. In most existing methods, fault diagnosis is performed solely based on features extracted by convolutional layers, with no additional layers utilized to enhance or refine these features. This work introduces a novel physics-based neural network for bearing fault diagnosis, in which specific layers are designed based on signal processing methods to extract the physical features of faults. These layers, referred to as physics-based layers, are constructed using adaptive analytical wavelet filterbanks. The features extracted by these layers are then classified using convolutional layers, enabling the diagnosis of bearing faults. A key advantage of this physics-based network is that it does not rely on a fixed architecture for feature extraction and classification. Instead, the characteristics of the network layers adapt to the fault characteristics present in the bearing vibration signals. The classification accuracy of the proposed method has been evaluated using experimental data from two studied cases. The results demonstrate that the newly introduced network achieves higher accuracy in classifying bearing signals with different faults compared to similar methods.
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Keywords
Filterbank, CNN, Physics-based Neural Networks, Ball Bearing, Wavelet Transform, Fault Diagnosis
References
Ahmed, H., & Nandi, A. K. (2020). Condition monitoring with vibration signals: Compressive sampling and learning algorithms for rotating machines. John Wiley & Sons.
Albezzawy, M., Nassef, M., Elsayed, E., & Elkhatib, A. (2019). Early rolling bearing fault detection using a Gini index guided adaptive Morlet wavelet filter. 2019 IEEE 10th International Conference on Mechanical and Aerospace Engineering (ICMAE),
Albezzawy, M. N., Nassef, M. G., & Sawalhi, N. (2020). Rolling element bearing fault identification using a novel three-step adaptive and automated filtration scheme based on Gini index. ISA transactions, 101, 453-460. https://doi.org/https://doi.org/10.1016/j.isatra.2020.01.019
Asr, M. Y., Ettefagh, M. M., Hassannejad, R., & Razavi, S. N. (2017). Diagnosis of combined faults in rotary machinery by non-naive Bayesian approach. Mechanical Systems and Signal Processing, 85, 56-70. https://doi.org/https://doi.org/10.1016/j.ymssp.2016.08.005
Bayram, I. (2012). An analytic wavelet transform with a flexible time-frequency covering. IEEE Transactions on Signal Processing, 61(5), 1131-1142. https://doi.org/https://doi.org/10.1109/TSP.2012.2232655
Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., & He, Z. (2016). Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 70, 1-35. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.08.023
Cheng, Y., Lin, M., Wu, J., Zhu, H., & Shao, X. (2021). Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowledge-Based Systems, 216, 106796. https://doi.org/https://doi.org/10.1016/j.knosys.2021.106796
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2020). Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery. Structural Health Monitoring, 19(5), 1453-1470. https://doi.org/https://doi.org/10.1177/1475921719887496
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Expert Systems with Applications, 167, 114094. https://doi.org/https://doi.org/10.1016/j.eswa.2020.114094
Dibaj, A., Hassannejad, R., Ettefagh, M. M., & Ehghaghi, M. B. (2021). Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold. ISA transactions, 114, 413-433. https://doi.org/https://doi.org/10.1016/j.isatra.2020.12.041
Fei, N., Gao, Y., Lu, Z., & Xiang, T. (2021). Z-score normalization, hubness, and few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision,
Feldman, M. (2009). Analytical basics of the EMD: Two harmonics decomposition. Mechanical Systems and Signal Processing, 23(7), 2059-2071. https://doi.org/https://doi.org/10.1016/j.ymssp.2009.04.002
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2018). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316-7325. https://doi.org/10.1109/TIE.2018.2877090
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision,
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition,
Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723-4741.
Jafarizadeh, M., Hassannejad, R., Ettefagh, M., & Chitsaz, S. (2008). Asynchronous input gear damage diagnosis using time averaging and wavelet filtering. Mechanical Systems and Signal Processing, 22(1), 172-201. https://doi.org/https://doi.org/10.1016/j.ymssp.2007.06.006
Ji, M., Peng, G., Li, S., Cheng, F., Chen, Z., Li, Z., & Du, H. (2022). A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis. Applied Soft Computing, 127, 109331. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109331
Kim, Y., & Kim, Y.-K. (2024). Physics-informed time-frequency fusion network with attention for noise-robust bearing fault diagnosis. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3355268
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.106587
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. PHM Society European Conference,
Li, J., Yao, X., Wang, X., Yu, Q., & Zhang, Y. (2020). Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis. Measurement, 153, 107419. https://doi.org/https://doi.org/10.1016/j.measurement.2019.107419
Liao, J.-X., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2025). Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions. Mechanical Systems and Signal Processing, 222, 111750. https://doi.org/https://doi.org/10.1016/j.ymssp.2024.111750
Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18(5), 1077-1095. https://doi.org/https://doi.org/10.1016/S0888-3270(03)00077-3
Ma, Y., Yang, J., & Li, L. (2022). Collaborative and adversarial deep transfer auto-encoder for intelligent fault diagnosis. Neurocomputing, 486, 1-15. https://doi.org/https://doi.org/10.1016/j.neucom.2022.02.050
Ma, Z., Fu, L., Xu, F., & Zhang, L. (2025). A physics-based sample generation method for few-shot bearing condition monitoring. Knowledge-Based Systems, 310, 112952. https://doi.org/https://doi.org/10.1016/j.knosys.2024.112952
Mao, W., Ding, L., Liu, Y., Afshari, S. S., & Liang, X. (2022). A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault. ISA transactions, 122, 444-458. https://doi.org/https://doi.org/10.1016/j.isatra.2021.04.026
Maruthi, G., & Hegde, V. (2015). Application of MEMS accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor. IEEE Sensors Journal, 16(1), 145-152. https://doi.org/10.1109/JSEN.2015.2476561
Mohanty, A. R. (2014). Machinery condition monitoring: Principles and practices. CRC Press.
Pestana-Viana, D., Zambrano-López, R., De Lima, A. A., Prego, T. d. M., Netto, S. L., & da Silva, E. A. (2016). The influence of feature vector on the classification of mechanical faults using neural networks. 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS),
Qian, C., Zhu, J., Shen, Y., Jiang, Q., & Zhang, Q. (2022). Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Processing Letters, 54(3), 2509-2531. https://doi.org/https://doi.org/10.1007/s11063-021-10719-z
Qin, Y., Xing, J., & Mao, Y. (2016). Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis. Measurement Science and Technology, 27(8), 085003. https://doi.org/https://doi.org/10.1088/0957-0233/27/8/085003
Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289-306. https://doi.org/https://doi.org/10.1016/j.triboint.2015.12.037
Randall, R. B. (2021). Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications. John Wiley & Sons.
Sadoughi, M., & Hu, C. (2019). Physics-based convolutional neural network for fault diagnosis of rolling element bearings. IEEE Sensors Journal, 19(11), 4181-4192. https://doi.org/10.1109/JSEN.2019.2898634
Sharma, S., Tiwari, S., & Singh, S. (2021). Integrated approach based on flexible analytical wavelet transform and permutation entropy for fault detection in rotary machines. Measurement, 169, 108389. https://doi.org/https://doi.org/10.1016/j.measurement.2020.108389
Shen, F., Chen, C., Yan, R., & Gao, R. X. (2015). Bearing fault diagnosis based on SVD feature extraction and transfer learning classification. 2015 prognostics and system health management conference (PHM),
Shen, S., Lu, H., Sadoughi, M., Hu, C., Nemani, V., Thelen, A., Webster, K., Darr, M., Sidon, J., & Kenny, S. (2021). A physics-informed deep learning approach for bearing fault detection. Engineering Applications of Artificial Intelligence, 103, 104295. https://doi.org/https://doi.org/10.1016/j.engappai.2021.104295
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64, 100-131. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.04.021
Sun, M., Wang, H., Liu, P., Huang, S., & Fan, P. (2019). A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings. Measurement, 146, 305-314. https://doi.org/https://doi.org/10.1016/j.measurement.2019.06.029
Tian, J., Morillo, C., Azarian, M. H., & Pecht, M. (2015). Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics, 63(3), 1793-1803. https://doi.org/10.1109/TIE.2015.2509913
Wang, D. (2018). Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients. Mechanical Systems and Signal Processing, 108, 360-368. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.02.034
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 122, 692-706. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.12.051
Yang, J., & Delpha, C. (2022a). An incipient fault diagnosis methodology using local Mahalanobis distance: Detection process based on empirical probability density estimation. Signal Processing, 190, 108308. https://doi.org/https://doi.org/10.1016/j.sigpro.2021.108308
Yang, J., & Delpha, C. (2022b). An incipient fault diagnosis methodology using local Mahalanobis distance: Fault isolation and fault severity estimation. Signal Processing, 200, 108657. https://doi.org/https://doi.org/10.1016/j.sigpro.2022.108657
Zhang, C., Li, B., Chen, B., Cao, H., Zi, Y., & He, Z. (2015). Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mechanical Systems and Signal Processing, 64, 162-187. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.03.030
Zhang, C., Yuling, L., Fangyi, W., Binqiang, C., Jie, L., & Bingbing, H. (2020). Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases. Chinese Journal of Aeronautics, 33(2), 407-417. https://doi.org/https://doi.org/10.1016/j.cja.2019.03.014
Zhang, X., Liu, Z., Wang, J., & Wang, J. (2019). Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. ISA transactions, 87, 225-234. https://doi.org/https://doi.org/10.1016/j.isatra.2018.11.033
Zhang, Y., Lv, Y., & Ge, M. (2021). Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis. Energy Reports, 7, 2418-2435. https://doi.org/https://doi.org/10.1016/j.egyr.2021.04.045
Albezzawy, M., Nassef, M., Elsayed, E., & Elkhatib, A. (2019). Early rolling bearing fault detection using a Gini index guided adaptive Morlet wavelet filter. 2019 IEEE 10th International Conference on Mechanical and Aerospace Engineering (ICMAE),
Albezzawy, M. N., Nassef, M. G., & Sawalhi, N. (2020). Rolling element bearing fault identification using a novel three-step adaptive and automated filtration scheme based on Gini index. ISA transactions, 101, 453-460. https://doi.org/https://doi.org/10.1016/j.isatra.2020.01.019
Asr, M. Y., Ettefagh, M. M., Hassannejad, R., & Razavi, S. N. (2017). Diagnosis of combined faults in rotary machinery by non-naive Bayesian approach. Mechanical Systems and Signal Processing, 85, 56-70. https://doi.org/https://doi.org/10.1016/j.ymssp.2016.08.005
Bayram, I. (2012). An analytic wavelet transform with a flexible time-frequency covering. IEEE Transactions on Signal Processing, 61(5), 1131-1142. https://doi.org/https://doi.org/10.1109/TSP.2012.2232655
Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., & He, Z. (2016). Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 70, 1-35. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.08.023
Cheng, Y., Lin, M., Wu, J., Zhu, H., & Shao, X. (2021). Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowledge-Based Systems, 216, 106796. https://doi.org/https://doi.org/10.1016/j.knosys.2021.106796
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2020). Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery. Structural Health Monitoring, 19(5), 1453-1470. https://doi.org/https://doi.org/10.1177/1475921719887496
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Expert Systems with Applications, 167, 114094. https://doi.org/https://doi.org/10.1016/j.eswa.2020.114094
Dibaj, A., Hassannejad, R., Ettefagh, M. M., & Ehghaghi, M. B. (2021). Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold. ISA transactions, 114, 413-433. https://doi.org/https://doi.org/10.1016/j.isatra.2020.12.041
Fei, N., Gao, Y., Lu, Z., & Xiang, T. (2021). Z-score normalization, hubness, and few-shot learning. Proceedings of the IEEE/CVF International Conference on Computer Vision,
Feldman, M. (2009). Analytical basics of the EMD: Two harmonics decomposition. Mechanical Systems and Signal Processing, 23(7), 2059-2071. https://doi.org/https://doi.org/10.1016/j.ymssp.2009.04.002
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2018). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316-7325. https://doi.org/10.1109/TIE.2018.2877090
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision,
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition,
Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723-4741.
Jafarizadeh, M., Hassannejad, R., Ettefagh, M., & Chitsaz, S. (2008). Asynchronous input gear damage diagnosis using time averaging and wavelet filtering. Mechanical Systems and Signal Processing, 22(1), 172-201. https://doi.org/https://doi.org/10.1016/j.ymssp.2007.06.006
Ji, M., Peng, G., Li, S., Cheng, F., Chen, Z., Li, Z., & Du, H. (2022). A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis. Applied Soft Computing, 127, 109331. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109331
Kim, Y., & Kim, Y.-K. (2024). Physics-informed time-frequency fusion network with attention for noise-robust bearing fault diagnosis. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3355268
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. https://doi.org/https://doi.org/10.1016/j.ymssp.2019.106587
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. PHM Society European Conference,
Li, J., Yao, X., Wang, X., Yu, Q., & Zhang, Y. (2020). Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis. Measurement, 153, 107419. https://doi.org/https://doi.org/10.1016/j.measurement.2019.107419
Liao, J.-X., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2025). Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions. Mechanical Systems and Signal Processing, 222, 111750. https://doi.org/https://doi.org/10.1016/j.ymssp.2024.111750
Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18(5), 1077-1095. https://doi.org/https://doi.org/10.1016/S0888-3270(03)00077-3
Ma, Y., Yang, J., & Li, L. (2022). Collaborative and adversarial deep transfer auto-encoder for intelligent fault diagnosis. Neurocomputing, 486, 1-15. https://doi.org/https://doi.org/10.1016/j.neucom.2022.02.050
Ma, Z., Fu, L., Xu, F., & Zhang, L. (2025). A physics-based sample generation method for few-shot bearing condition monitoring. Knowledge-Based Systems, 310, 112952. https://doi.org/https://doi.org/10.1016/j.knosys.2024.112952
Mao, W., Ding, L., Liu, Y., Afshari, S. S., & Liang, X. (2022). A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault. ISA transactions, 122, 444-458. https://doi.org/https://doi.org/10.1016/j.isatra.2021.04.026
Maruthi, G., & Hegde, V. (2015). Application of MEMS accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor. IEEE Sensors Journal, 16(1), 145-152. https://doi.org/10.1109/JSEN.2015.2476561
Mohanty, A. R. (2014). Machinery condition monitoring: Principles and practices. CRC Press.
Pestana-Viana, D., Zambrano-López, R., De Lima, A. A., Prego, T. d. M., Netto, S. L., & da Silva, E. A. (2016). The influence of feature vector on the classification of mechanical faults using neural networks. 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS),
Qian, C., Zhu, J., Shen, Y., Jiang, Q., & Zhang, Q. (2022). Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Processing Letters, 54(3), 2509-2531. https://doi.org/https://doi.org/10.1007/s11063-021-10719-z
Qin, Y., Xing, J., & Mao, Y. (2016). Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis. Measurement Science and Technology, 27(8), 085003. https://doi.org/https://doi.org/10.1088/0957-0233/27/8/085003
Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289-306. https://doi.org/https://doi.org/10.1016/j.triboint.2015.12.037
Randall, R. B. (2021). Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications. John Wiley & Sons.
Sadoughi, M., & Hu, C. (2019). Physics-based convolutional neural network for fault diagnosis of rolling element bearings. IEEE Sensors Journal, 19(11), 4181-4192. https://doi.org/10.1109/JSEN.2019.2898634
Sharma, S., Tiwari, S., & Singh, S. (2021). Integrated approach based on flexible analytical wavelet transform and permutation entropy for fault detection in rotary machines. Measurement, 169, 108389. https://doi.org/https://doi.org/10.1016/j.measurement.2020.108389
Shen, F., Chen, C., Yan, R., & Gao, R. X. (2015). Bearing fault diagnosis based on SVD feature extraction and transfer learning classification. 2015 prognostics and system health management conference (PHM),
Shen, S., Lu, H., Sadoughi, M., Hu, C., Nemani, V., Thelen, A., Webster, K., Darr, M., Sidon, J., & Kenny, S. (2021). A physics-informed deep learning approach for bearing fault detection. Engineering Applications of Artificial Intelligence, 103, 104295. https://doi.org/https://doi.org/10.1016/j.engappai.2021.104295
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64, 100-131. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.04.021
Sun, M., Wang, H., Liu, P., Huang, S., & Fan, P. (2019). A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings. Measurement, 146, 305-314. https://doi.org/https://doi.org/10.1016/j.measurement.2019.06.029
Tian, J., Morillo, C., Azarian, M. H., & Pecht, M. (2015). Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics, 63(3), 1793-1803. https://doi.org/10.1109/TIE.2015.2509913
Wang, D. (2018). Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients. Mechanical Systems and Signal Processing, 108, 360-368. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.02.034
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 122, 692-706. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.12.051
Yang, J., & Delpha, C. (2022a). An incipient fault diagnosis methodology using local Mahalanobis distance: Detection process based on empirical probability density estimation. Signal Processing, 190, 108308. https://doi.org/https://doi.org/10.1016/j.sigpro.2021.108308
Yang, J., & Delpha, C. (2022b). An incipient fault diagnosis methodology using local Mahalanobis distance: Fault isolation and fault severity estimation. Signal Processing, 200, 108657. https://doi.org/https://doi.org/10.1016/j.sigpro.2022.108657
Zhang, C., Li, B., Chen, B., Cao, H., Zi, Y., & He, Z. (2015). Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mechanical Systems and Signal Processing, 64, 162-187. https://doi.org/https://doi.org/10.1016/j.ymssp.2015.03.030
Zhang, C., Yuling, L., Fangyi, W., Binqiang, C., Jie, L., & Bingbing, H. (2020). Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases. Chinese Journal of Aeronautics, 33(2), 407-417. https://doi.org/https://doi.org/10.1016/j.cja.2019.03.014
Zhang, X., Liu, Z., Wang, J., & Wang, J. (2019). Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. ISA transactions, 87, 225-234. https://doi.org/https://doi.org/10.1016/j.isatra.2018.11.033
Zhang, Y., Lv, Y., & Ge, M. (2021). Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis. Energy Reports, 7, 2418-2435. https://doi.org/https://doi.org/10.1016/j.egyr.2021.04.045
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Technical Papers