
Zhang, X., Zhao, B. & Lin, Y. Machine learning based bearing fault diagnosis using the case western reserve university data: A review. IEEE Access 9, 155598–155608 (2021).
Google Scholar
Meng, Z., Cui, Z., Liu, J., Li, J. & Fan, F. Maximum cyclic gini index deconvolution for rolling bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Tang, X., Xu, Z. & Wang, Z. A novel fault diagnosis method of rolling bearing based on integrated vision transformer model. Sensors 22(10), 3878 (2022).
Google Scholar
Shenfield, A. & Howarth, M. A novel deep learning model for the detection and identification of rolling element-bearing faults. Sensors 20(18), 5112 (2020).
Google Scholar
Qi, B., Li, Y., Yao, W. & Li, Z. Application of emd combined with deep learning and knowledge graph in bearing fault. J. Signal Process. Syst. 1, 1–20 (2023).
Google Scholar
Jin, Y., Hou, L. & Chen, Y. A time series transformer based method for the rotating machinery fault diagnosis. Neurocomputing 494, 379–395 (2022).
Google Scholar
Han, T., Pang, J. & Tan, A. C. Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network. J. Manuf. Syst. 61, 576–591 (2021).
Google Scholar
Zhang, J., Chen, J., Deng, H. & Hu, W. A novel framework based on adaptive multi-task learning for bearing fault diagnosis. Energy Rep. 9, 522–531 (2023).
Google Scholar
Ghorvei, M., Kavianpour, M., Beheshti, M. T. & Ramezani, A. Synthetic to real framework based on convolutional multi-head attention and hybrid domain alignment. In 2022 8th International Conference on Control, Instrumentation and Automation (ICCIA) 1–6 (IEEE, 2022).
Rajput, D. S., Meena, G., Acharya, M. & Mohbey, K. K. Fault prediction using fuzzy convolution neural network on iot environment with heterogeneous sensing data fusion. Meas. Sens. 26, 100701 (2023).
Google Scholar
Hou, Y., Wang, J., Chen, Z., Ma, J. & Li, T. Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved transformer. Eng. Appl. Artif. Intell. 124, 106507 (2023).
Google Scholar
Yang, D., Karimi, H. R. & Gelman, L. An explainable intelligence fault diagnosis framework for rotating machinery. Neurocomputing 541, 126257 (2023).
Google Scholar
Magar, R., Ghule, L., Li, J., Zhao, Y. & Farimani, A. B. Faultnet: A deep convolutional neural network for bearing fault classification. IEEE Access 9, 25189–25199 (2021).
Google Scholar
Wang, H., Zhang, W., Yang, D. & Xiang, Y. Deep-learning-enabled predictive maintenance in industrial internet of things: Methods, applications, and challenges. IEEE Syst. J. 17, 2602 (2022).
Google Scholar
Alonso-González, M. et al. Bearing fault diagnosis with envelope analysis and machine learning approaches using cwru dataset. IEEE Access 11, 57796 (2023).
Google Scholar
Tang, L., Wu, X., Wang, D. & Liu, X. A comparative experimental study of vibration and acoustic emission on fault diagnosis of low-speed bearing. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Cateni, S. et al. Variable selection through genetic algorithms for classification purposes. In Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, vol. 1, 6–11 (AIA, 2010)
Heinze, G., Wallisch, C. & Dunkler, D. Variable selection—A review and recommendations for the practicing statistician. Biom. J. 60, 431. (2018).
Google Scholar
Tang, G., Hu, H., Kong, J. & Liu, H. A novel fault feature selection and diagnosis method for rotating machinery with symmetrized dot pattern representation. IEEE Sens. J. 23(2), 1447–1461 (2022).
Google Scholar
Lee, C.-Y., Le, T.-A. & Hung, C.-L. A feature selection approach based on memory space computation genetic algorithm applied in bearing fault diagnosis model. IEEE Access 11, 51282 (2023).
Google Scholar
Yang, Y., Liu, H., Han, L. & Gao, P. A feature extraction method using vmd and improved envelope spectrum entropy for rolling bearing fault diagnosis. IEEE Sens. J. 23(4), 3848–3858 (2023).
Google Scholar
Gu, J., Peng, Y., Lu, H., Chang, X. & Chen, G. A novel fault diagnosis method of rotating machinery via vmd, cwt and improved cnn. Measurement 200, 111635 (2022).
Google Scholar
Zhao, Y., Zhang, N., Zhang, Z. & Xu, X. Bearing fault diagnosis based on mel frequency cepstrum coefficient and deformable space-frequency attention network. IEEE Access 11, 34407–34420 (2023).
Google Scholar
Zhou, C. et al. A mechanical part fault diagnosis method based on improved multiscale weighted permutation entropy and multiclass lstsvm. Measurement 214, 112671 (2023).
Google Scholar
Kulevome, D. K. B., Wang, H. & Wang, X. Rolling bearing fault diagnostics based on improved data augmentation and convnet. J. Syst. Eng. Electron. 34(4), 1074–1084 (2023).
Google Scholar
Liu, X., Sun, W., Li, H., Wang, Z. & Li, Q. Imbalanced sample fault diagnosis of rolling bearing using deep condition multidomain generative adversarial network. IEEE Sens. J. 23(2), 1271–1285 (2022).
Google Scholar
Huo, J., Qi, C., Li, C. & Wang, N. Data augmentation fault diagnosis method based on residual mixed self-attention for rolling bearings under imbalanced samples. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Tong, J., Liu, C., Bao, J., Pan, H. & Zheng, J. A novel ensemble learning-based multisensor information fusion method for rolling bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1–12 (2022).
Google Scholar
Zhou, H. et al. Hob vibration signal denoising and effective features enhancing using improved complete ensemble empirical mode decomposition with adaptive noise and fuzzy rough sets. Expert Syst. Appl. 233, 120989 (2023).
Google Scholar
Xiong, J. et al. A bearing fault diagnosis method based on improved mutual dimensionless and deep learning. IEEE Sens. J. 23(16), 18338 (2023).
Google Scholar
Yu, W., Pi, D., Xie, L. & Luo, Y. Multiscale attentional residual neural network framework for remaining useful life prediction of bearings. Measurement 177, 109310 (2021).
Google Scholar
Hosna, A. et al. Transfer learning: A friendly introduction. J. Big Data 9(1), 102 (2022).
Google Scholar
Zhu, W., Shi, B., Feng, Z. & Tang, J. An unsupervised domain adaptation method for intelligent bearing fault diagnosis based on signal reconstruction by cycle-consistent adversarial learning. IEEE Sens. J. 1, 1 (2023).
Zhu, W., Shi, B. & Feng, Z. A transfer learning method using high-quality pseudo labels for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1–11 (2022).
Google Scholar
Yu, X. et al. A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions. Measurement 201, 111597 (2022).
Google Scholar
Ayodeji, A. et al. Causal augmented convnet: A temporal memory dilated convolution model for long-sequence time series prediction. ISA Trans. 123, 200–217 (2022).
Google Scholar
Liu, S., Chen, J., He, S., Shi, Z. & Zhou, Z. Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation. Mech. Syst. Signal Process. 189, 110071 (2023).
Google Scholar
Yu, X. et al. A new cross-domain bearing fault diagnosis framework based on transferable features and manifold embedded discriminative distribution adaption under class imbalance. IEEE Sens. J. 23(7), 7525–7545 (2023).
Google Scholar
Gao, H., Zhang, X., Gao, X., Li, F. & Han, H. Icot-gan: Integrated convolutional transformer gan for rolling bearings fault diagnosis under limited data condition. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Luo, P., Yin, Z., Yuan, D., Gao, F. & Liu, J. An intelligent method for early motor bearing fault diagnosis based on Wasserstein distance generative adversarial networks meta learning. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Ren, Z., Ji, J., Zhu, Y., Hong, J. & Feng, K. Generative adversarial network with dual multi-scale feature fusion for data augmentation in fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Lu, Z., Cai, Z., Qian, W. & Zhou, D. Intelligent fault diagnosis of bearings with both working condition variation and target data scarcity. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Azari, M. S., Flammini, F., Santini, S. & Caporuscio, M. A systematic literature review on transfer learning for predictive maintenance in industry 4.0. IEEE Access 11, 12887 (2023).
Google Scholar
Castano, F., Cruz, Y. J., Villalonga, A. & Haber, R. E. Data-driven insights on time-to-failure of electromechanical manufacturing devices: A procedure and case study. IEEE Trans. Ind. Inform. 19, 7190 (2022).
Google Scholar
Mao, W., Chen, J., Liu, J. & Liang, X. Self-supervised deep domain-adversarial regression adaptation for online remaining useful life prediction of rolling bearing under unknown working condition. IEEE Trans. Ind. Inf. 19(2), 1227–1237 (2022).
Google Scholar
Ni, Q., Ji, J. & Feng, K. Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network. IEEE Trans. Ind. Inf. 19(2), 1301–1311 (2022).
Google Scholar
Gao, H., Li, Y., Zhao, Y. & Song, Y. Dual channel feature-attention-based approach for rul prediction considering the spatiotemporal difference of multisensor data. IEEE Sens. J. 23, 8514 (2023).
Google Scholar
Yu, W., Liu, Y., Dillon, T. & Rahayu, W. Edge computing-assisted iot framework with an autoencoder for fault detection in manufacturing predictive maintenance. IEEE Trans. Ind. Inf. 19(4), 5701–5710 (2022).
Google Scholar
Zhao, C., Tang, B., Huang, Y. & Deng, L. Edge collaborative compressed sensing in wireless sensor networks for mechanical vibration monitoring. IEEE Trans. Ind. Inform. 19, 8852 (2022).
Google Scholar
Asutkar, S., Chalke, C., Shivgan, K. & Tallur, S. Tinyml-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis. Expert Syst. Appl. 213, 119016 (2023).
Google Scholar
Kamath, V. & Renuka, A. Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead. Neurocomputing 531, 34–60. (2023).
Google Scholar
Gutierrez-Torre, A. et al. Automatic distributed deep learning using resource-constrained edge devices. IEEE Internet Things J. 9(16), 15018–15029. (2022).
Google Scholar
Ren, Z. et al. A systematic review on imbalanced learning methods in intelligent fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Zhang, Q., Yuan, R., Lv, Y., Li, Z. & Wu, H. Multivariate dynamic mode decomposition and its application to bearing fault diagnosis. IEEE Sens. J. 23(7), 7514–7524 (2023).
Google Scholar
Niu, G., Liu, E., Wang, X., Ziehl, P. & Zhang, B. Enhanced discriminate feature learning deep residual cnn for multitask bearing fault diagnosis with information fusion. IEEE Trans. Ind. Inf. 19(1), 762–770 (2022).
Google Scholar
Brusamarello, B., Silva, J. C. C., Morais Sousa, K. & Guarneri, G. A. Bearing fault detection in three-phase induction motors using support vector machine and fiber Bragg grating. IEEE Sens. J. 23(5), 4413–4421 (2022).
Google Scholar
Liu, D., Cui, L. & Cheng, W. Flexible generalized demodulation for intelligent bearing fault diagnosis under nonstationary conditions. IEEE Trans. Ind. Inf. 19(3), 2717–2728 (2022).
Google Scholar
Zhang, X. et al. Inferable deep distilled attention network for diagnosing multiple motor bearing faults. IEEE Trans. Transp. Electrif. 9, 2207 (2022).
Google Scholar
Kim, T. & Lee, S. A novel unsupervised clustering and domain adaptation framework for rotating machinery fault diagnosis. IEEE Trans. Ind. Inform. 19, 9404 (2022).
Google Scholar
Zhang, W. et al. Deephealth: A self-attention based method for instant intelligent predictive maintenance in industrial internet of things. IEEE Trans. Ind. Inf. 17(8), 5461–5473 (2020).
Google Scholar
Meng, Z., Zhu, J., Cao, S., Li, P. & Xu, C. Bearing fault diagnosis under multi-sensor fusion based on modal analysis and graph attention network. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Chang, M., Yao, D. & Yang, J. Intelligent fault dignosis of rolling bearings using efficient and lightweight resnet networks based on an attention mechanism. IEEE Sens. J. 23, 9136 (2023).
Google Scholar
Xue, L., Lei, C., Jiao, M., Shi, J. & Li, J. Rolling bearing fault diagnosis method based on self-calibrated coordinate attention mechanism and multi-scale convolutional neural network under small samples. IEEE Sens. J. 23, 10206 (2023).
Google Scholar
Wang, H. et al. Fault diagnosis method for imbalanced data of rotating machinery based on time domain signal prediction and sc-resnest. IEEE Access 11, 38875 (2023).
Google Scholar
Wang, D., Li, Y., Jia, L., Song, Y. & Wen, T. Attention-based bilinear feature fusion method for bearing fault diagnosis. IEEE/ASME Trans. Mechatron. 28, 1695 (2022).
Google Scholar
Wang, X., Zhang, H. & Du, Z. Multi-scale noise reduction attention network for aero-engine bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Mao, W., Liu, K., Zhang, Y., Liang, X. & Wang, Z. Self-supervised deep tensor domain-adversarial regression adaptation for online remaining useful life prediction across machines. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Pu, H., Zhang, K. & An, Y. Restricted sparse networks for rolling bearing fault diagnosis. IEEE Trans. Ind. Inform. 19, 11139 (2023).
Google Scholar
Wan, S. et al. Bearing fault diagnosis based on multi-sensor information coupling and attentional feature fusion. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Meng, Z., Luo, C., Li, J., Cao, L. & Fan, F. Research on fault diagnosis of rolling bearing based on lightweight model with multiscale features. IEEE Sens. J. 23, 13236 (2023).
Google Scholar
Lee, C.-Y. & Zhuo, G.-L. Identifying bearing faults using multiscale residual attention and multichannel neural network. IEEE Access 11, 26953–26963 (2023).
Google Scholar
Ma, W., Zhang, Y., Ma, L., Liu, R. & Yan, S. An unsupervised domain adaptation approach with enhanced transferability and discriminability for bearing fault diagnosis under few-shot samples. Expert Syst. Appl. 225, 120084 (2023).
Google Scholar
Yan, X., Zhang, C.-A. & Liu, Y. Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system. Measurement 171, 108778 (2021).
Google Scholar
Buchaiah, S. & Shakya, P. Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection. Measurement 188, 110506. (2022).
Google Scholar
Yang, K., Zhao, L. & Wang, C. A new intelligent bearing fault diagnosis model based on triplet network and svm. Sci. Rep. 12, 5234 (2022).
Google Scholar
Shao, H. et al. Dual-threshold attention-guided gan and limited infrared thermal images for rotating machinery fault diagnosis under speed fluctuation. IEEE Trans. Ind. Inform. 19, 9933 (2023).
Google Scholar
Li, J. et al. A new probability guided domain adversarial network for bearing fault diagnosis. IEEE Sens. J. 23(2), 1462–1470 (2022).
Google Scholar
Han, B., Jiang, X., Wang, J. & Zhang, Z. A novel domain adaptive fault diagnosis method for bearings based on unbalance data generation. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Wang, D., Dong, Y., Wang, H. & Tang, G. Limited fault data augmentation with compressed sensing for bearing fault diagnosis. IEEE Sens. J. 23(13), 14499 (2023).
Google Scholar
Ren, H., Wang, J., Shen, C., Huang, W. & Zhu, Z. Dual classifier-discriminator adversarial networks for open set fault diagnosis of train bearings. IEEE Sens. J. 1, 1 (2023).
Google Scholar
Su, Z. et al. Cross-domain open-set fault diagnosis based on target domain slanted adversarial network for rotating machinery. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Liu, S., Jiang, H., Wu, Z., Liu, Y. & Zhu, K. Machine fault diagnosis with small sample based on variational information constrained generative adversarial network. Adv. Eng. Inform. 54, 101762 (2022).
Google Scholar
Dai, Z., Zhao, L., Wang, K. & Zhou, Y. Generative adversarial network to alleviate information insufficiency in intelligent fault diagnosis by generating continuations of signals. Appl. Soft Comput. 147, 110784 (2023).
Google Scholar
Chen, Q. et al. A lightweight and robust model for engineering cross-domain fault diagnosis via feature fusion-based unsupervised adversarial learning. Measurement 205, 112139 (2022).
Google Scholar
Li, J., Liu, Y. & Li, Q. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method. Measurement 189, 110500. (2022).
Google Scholar
Zhang, J., Zhang, K., An, Y., Luo, H. & Yin, S. An integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning under imbalanced sample condition. IEEE Trans. Neural Netw. Learn. Syst. 1, 1–12 (2023).
Google Scholar
Liu, X. et al. Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data. Knowl. Based Syst. 251, 109272. (2022).
Google Scholar
Ding, Y. et al. Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions. Reliab. Eng. Syst. Saf. 230, 108890. (2023).
Google Scholar
Wang, X., Jiang, H., Liu, Y., Liu, S. & Yang, Q. A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data. Eng. Appl. Artif. Intell. 126, 106872. (2023).
Google Scholar
Liu, X. et al. A fault diagnosis method of rolling bearing based on improved recurrence plot and convolutional neural network. IEEE Sens. J. 23, 10767 (2023).
Google Scholar
Yuan, Z., Ma, Z., Li, X. & Li, J. A multichannel mn-gcn for wheelset-bearing system fault diagnosis. IEEE Sens. J. 23(3), 2481–2494 (2022).
Google Scholar
Lyu, P., Zhang, K., Yu, W., Wang, B. & Liu, C. A novel rsg-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment. Adv. Eng. Inform. 52, 101564. (2022).
Google Scholar
Liang, P. et al. Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved resnet under noisy labels and environment. Eng. Appl. Artif. Intell. 115, 105269. (2022).
Google Scholar
Alfarizi, M. G., Tajiani, B., Vatn, J. & Yin, S. Optimized random forest model for remaining useful life prediction of experimental bearings. IEEE Trans. Ind. Inform. 19, 7771 (2022).
Google Scholar
Kumar, A., Parkash, C., Tang, H. & Xiang, J. Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (rul) of bearing. Adv. Eng. Inform. 58, 102206 (2023).
Google Scholar
Hua, L., Wu, X., Liu, T. & Li, S. The methodology of modified frequency band envelope kurtosis for bearing fault diagnosis. IEEE Trans. Ind. Inf. 19(3), 2856–2865 (2022).
Google Scholar
Li, Y., Zhou, J., Li, H., Meng, G. & Bian, J. A fast and adaptive empirical mode decomposition method and its application in rolling bearing fault diagnosis. IEEE Sens. J. 23(1), 567–576 (2022).
Google Scholar
Chen, Z. et al. Feature extraction based on hierarchical improved envelope spectrum entropy for rolling bearing fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Zhou, Q., Yi, C., Yan, L., Huang, C. & Lin, J. A blind deconvolution approach based on spectral harmonics-to-noise ratio for rotating machinery condition monitoring. IEEE Trans. Autom. Sci. Eng. 20(2), 1092–1107 (2022).
Google Scholar
Chen, R., Huang, Y., Xu, X., Zhang, X. & Qiu, T. Rolling bearing fault feature extraction method using adaptive maximum cyclostationarity blind deconvolution. IEEE Sens. J. 23, 17761 (2023).
Google Scholar
Li, J., Liu, Y. & Xiang, J. Optimal maximum cyclostationary blind deconvolution for bearing fault detection. IEEE Sens. J. 23, 15975 (2023).
Google Scholar
Yi, C. et al. An adaptive harmonic product spectrum for rotating machinery fault diagnosis. IEEE Trans. Instrum. Meas. 72, 1–12 (2022).
Google Scholar
Pan, H., Xu, H. & Zheng, J. A novel symplectic relevance matrix machine method for intelligent fault diagnosis of roller bearing. Expert Syst. Appl. 192, 116400 (2022).
Google Scholar
Ma, C., Yang, Z., Zhang, K., Xiang, L. & Xu, Y. Optimization of Ramanujan subspace periodic and its application in identifying industrial bearing fault features. IEEE Trans. Instrum. Meas. 72, 1–7 (2022).
Google Scholar
Mitra, S. & Koley, C. Early and intelligent bearing fault detection using adaptive superlets. IEEE Sens. J. 23(7), 7992–8000 (2023).
Google Scholar
Zhao, H. et al. Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network. IEEE Trans. Reliab. 72, 692 (2022).
Google Scholar
Xue, Y., Yang, R., Chen, X., Tian, Z. & Wang, Z. A novel local binary temporal convolutional neural network for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Cui, X. et al. A novel fault diagnosis method for rotor-bearing system based on instantaneous orbit fusion feature image and deep convolutional neural network. IEEE/ASME Trans. Mechatron. 28(2), 1013–1024 (2022).
Google Scholar
Zhang, B., Pang, X., Zhao, P. & Lu, K. A new method based on encoding data probability density and convolutional neural network for rotating machinery fault diagnosis. IEEE Access 11, 26099–26113 (2023).
Google Scholar
Li, Q. et al. Fault diagnosis of bearings and gears based on litenet with feature aggregation. IEEE Trans. Instrum. Meas. 72, 1–9 (2023).
Liang, H., Cao, J. & Zhao, X. Multibranch and multiscale dynamic convolutional network for small sample fault diagnosis of rotating machinery. IEEE Sens. J. 23(8), 8973–8988 (2023).
Google Scholar
Liu, X., Lu, J. & Li, Z. Multi-scale fusion attention convolutional neural network for fault diagnosis of aero-engine rolling bearing. IEEE Sens. J. 1, 1 (2023).
Google Scholar
Cheng, L. et al. S3m: Two-stage-based semi-self-supervised method for intelligent bearing fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Tang, H. et al. Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter. Eng. Appl. Artif. Intell. 127, 107138 (2024).
Google Scholar
Huo, C. et al. A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions. Appl. Soft Comput. 146, 110739 (2023).
Google Scholar
Li, F., Wang, L., Wang, D., Wu, J. & Zhao, H. An adaptive multiscale fully convolutional network for bearing fault diagnosis under noisy environments. Measurement 216, 112993 (2023).
Google Scholar
Huo, C., Jiang, Q., Shen, Y., Zhu, Q. & Zhang, Q. Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network. Eng. Appl. Artif. Intell. 121, 105970 (2023).
Google Scholar
Zhao, X. et al. Multiscale deep graph convolutional networks for intelligent fault diagnosis of rotor-bearing system under fluctuating working conditions. IEEE Trans. Ind. Inf. 19(1), 166–176 (2022).
Google Scholar
Feng, K. et al. Digital twin enabled domain adversarial graph networks for bearing fault diagnosis. IEEE Trans. Ind. Cyber Phys. Syst. 1, 1 (2023).
Google Scholar
Lu, F., Tong, Q., Feng, Z. & Wan, Q. Unbalanced bearing fault diagnosis under various speeds based on spectrum alignment and deep transfer convolution neural network. IEEE Trans. Ind. Inform. 19, 8295 (2022).
Google Scholar
Yang, S., Cui, Z. & Gu, X. A balanced deep transfer network for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Li, X., Hu, H., Zhang, S. & Tang, G. A fault diagnosis method for rotating machinery with semi-supervised graph convolutional network and images converted from vibration signals. IEEE Sens. J. 23, 11946 (2023).
Google Scholar
Yin, P. et al. A multi-scale graph convolutional neural network framework for fault diagnosis of rolling bearing. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Chen, P., Zhao, R., He, T., Wei, K. & Yuan, J. Unsupervised structure subdomain adaptation based the contrastive cluster center for bearing fault diagnosis. Eng. Appl. Artif. Intell. 122, 106141 (2023).
Google Scholar
Zhu, J. et al. Application of recurrent neural network to mechanical fault diagnosis: A review. J. Mech. Sci. Technol. 36(2), 527–542 (2022).
Google Scholar
Imamura, L., Avila, S., Pacheco, F., Salles, M. & Jablon, L. Diagnosis of unbalance in lightweight rotating machines using a recurrent neural network suitable for an edge-computing framework. J. Control Autom. Electr. Syst. 33(4), 1272–1285 (2022).
Google Scholar
Chang, Y., Chen, J., Lv, H. & Liu, S. Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery. ISA Trans. 122, 409–423. (2022).
Google Scholar
Zhang, Z. et al. Attention gate guided multiscale recursive fusion strategy for deep neural network-based fault diagnosis. Eng. Appl. Artif. Intell. 126, 107052. (2023).
Google Scholar
Sun, H., Yang, B. & Lin, S. An open set diagnosis method for rolling bearing faults based on prototype and reconstructed integrated network. IEEE Trans. Instrum. Meas. 72, 1–10 (2022).
Google Scholar
Li, C. et al. Incrementally contrastive learning of homologous and interclass features for the fault diagnosis of rolling element bearings. IEEE Trans. Ind. Inform. 19, 11182 (2023).
Google Scholar
Wang, N. et al. Manifold-contrastive broad learning system for wheelset bearing fault diagnosis. IEEE Trans. Intell. Transp. Syst. 24, 9886 (2023).
Google Scholar
Zhu, Z. et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement 1, 112346 (2022).
Xu, Z. et al. Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors. Renew. Energy 182, 615–626. (2022).
Google Scholar
Shi, J. et al. Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks. Mech. Syst. Signal Process. 162, 107996. (2022).
Google Scholar
An, Y., Zhang, K., Liu, Q., Chai, Y. & Huang, X. Rolling bearing fault diagnosis method base on periodic sparse attention and lstm. IEEE Sens. J. 22(12), 12044–12053. (2022).
Google Scholar
Zhi Tang, X. L., Bo, Lin & Wei, D. A semi-supervised transferable lstm with feature evaluation for fault diagnosis of rotating machinery. Appl. Intell. 52, 1703–1717. (2022).
Google Scholar
Zhu, S. et al. A transformer model with enhanced feature learning and its application in rotating machinery diagnosis. ISA Trans. 133, 1–12 (2023).
Google Scholar
Xu, P. & Zhang, L. A fault diagnosis method for rolling bearing based on 1d-vit model. IEEE Access 11, 39664 (2023).
Google Scholar
Fang, H. et al. A lightweight transformer with strong robustness application in portable bearing fault diagnosis. IEEE Sens. J. 23, 9649 (2023).
Google Scholar
Wu, H., Triebe, M. J. & Sutherland, J. W. A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application. J. Manuf. Syst. 67, 439–452 (2023).
Google Scholar
Sun, Z., Wang, Y. & Gao, J. Intelligent fault diagnosis of rotating machinery under varying working conditions with global-local neighborhood and sparse graphs embedding deep regularized autoencoder. Eng. Appl. Artif. Intell. 124, 106590 (2023).
Google Scholar
Shi, M. et al. Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis. Knowl. Based Syst. 260, 110172 (2023).
Google Scholar
Shi, M. et al. Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery. Reliab. Eng. Syst. Saf. 240, 109601 (2023).
Google Scholar
Chen, X., Guo, Y. & Na, J. Instantaneous-angular-speed-based synchronous averaging tool for bearing outer race fault diagnosis. IEEE Trans. Ind. Electron. 70(6), 6250–6260 (2022).
Google Scholar
Gwak, M., Kim, M. S., Yun, J. P. & Park, P. Robust and explainable fault diagnosis with power-perturbation-based decision boundary analysis of deep learning models. IEEE Trans. Ind. Inform. 19, 6982 (2022).
Google Scholar
Chen, C., Shi, J., Shen, M., Feng, L. & Tao, G. A predictive maintenance strategy using deep learning quantile regression and kernel density estimation for failure prediction. IEEE Trans. Instrum. Meas. 72, 1–12 (2023).
Hongwei, F., Ceyi, X., Jiateng, M., Xiangang, C. & Xuhui, Z. A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and cnn-svm. Meas. Sci. Technol. 34(4), 044008. (2023).
Google Scholar
Lee, S. & Kim, T. Impact of deep learning optimizers and hyperparameter tuning on the performance of bearing fault diagnosis. IEEE Access 11, 55046–55070. (2023).
Google Scholar
Ye, X., Gao, L., Li, X. & Wen, L. A new hyper-parameter optimization method for machine learning in fault classification. Appl. Intell. 53(11), 14182–14200 (2023).
Google Scholar
Zhang, Y., Liu, W., Wang, X. & Shaheer, M. A. A novel hierarchical hyper-parameter search algorithm based on greedy strategy for wind turbine fault diagnosis. Expert Syst. Appl. 202, 117473. (2022).
Google Scholar
Zhang, M., Yin, J. & Chen, W. Rolling bearing fault diagnosis based on time-frequency feature extraction and iba-svm. IEEE Access 10, 85641–85654. (2022).
Google Scholar
Wen, L., Xie, X., Li, X. & Gao, L. A new ensemble convolutional neural network with diversity regularization for fault diagnosis. J. Manuf. Syst. 62, 964–971. (2022).
Google Scholar
Chen, R., Zhu, Y., Yang, L., Hu, X. & Chen, G. Adaptation regularization based on transfer learning for fault diagnosis of rotating machinery under multiple operating conditions. IEEE Sens. J. 22(11), 10655–10662. (2022).
Google Scholar
Hu, Q., Si, X., Qin, A., Lv, Y. & Liu, M. Balanced adaptation regularization based transfer learning for unsupervised cross-domain fault diagnosis. IEEE Sens. J. 22(12), 12139–12151. (2022).
Google Scholar
Lyu, P., Zhang, H., Yu, W. & Liu, C. A novel model-independent data augmentation method for fault diagnosis in smart manufacturing. In Leading Manufacturing Systems Transformation—Proceedings of the 55th CIRP Conference on Manufacturing Systems 949–954 (2022).
Shi, D., Ye, Y., Gillwald, M. & Hecht, M. Robustness enhancement of machine fault diagnostic models for railway applications through data augmentation. Mech. Syst. Signal Process. 164, 108217. (2022).
Google Scholar
Ai, T. et al. Fully simulated-data-driven transfer-learning method for rolling-bearing-fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Su, H., Xiang, L., Hu, A., Xu, Y. & Yang, X. A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions. Mech. Syst. Signal Process. 169, 108765. (2022).
Google Scholar
Ma, R., Han, T. & Lei, W. Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module. Knowl. Based Syst. 261, 110175. (2023).
Google Scholar
Qian, Q., Zhou, J. & Qin, Y. Relationship transfer domain generalization network for rotating machinery fault diagnosis under different working conditions. IEEE Trans. Ind. Inform. 19, 9898 (2023).
Google Scholar
Fang, H., Liu, H., Wang, X., Deng, J. & An, J. The method based on clustering for unknown failure diagnosis of rolling bearings. IEEE Trans. Instrum. Meas. 72, 1–8 (2023).
Google Scholar
Liu, X., Sun, W., Li, H., Li, Q. & Lv, S. A fusing domain feature and sharing label space based fault diagnosis approach for different distribution and unlabeled rolling bearing sample. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Liu, Y. et al. A lifelong learning method based on generative feature replay for bearing diagnosis with incremental fault types. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Yue, K., Li, J., Chen, Z., Huang, R. & Li, W. Multiple source-free domain adaptation network based on knowledge distillation for machinery fault diagnosis. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Li, Y., Dong, Y., Xu, M., Liu, P. & Wang, R. Instance weighting based partial domain adaptation for intelligent fault diagnosis of rotating machinery. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Ma, W., Liu, R., Guo, J., Wang, Z. & Ma, L. A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samples. Appl. Soft Comput. 140, 110243 (2023).
Google Scholar
Gao, Q., Huang, T., Zhao, K., Shao, H. & Jin, B. Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis. Expert Syst. Appl. 237, 121585 (2024).
Google Scholar
Jiang, Y., Xia, T., Wang, D., Zhang, K. & Xi, L. Joint adaptive transfer learning network for cross-domain fault diagnosis based on multi-layer feature fusion. Neurocomputing 487, 228–242 (2022).
Google Scholar
Liu, G., Shen, W., Gao, L. & Kusiak, A. Automated broad transfer learning for cross-domain fault diagnosis. J. Manuf. Syst. 66, 27–41 (2023).
Google Scholar
Li, W., Shang, Z., Gao, M., Liu, F. & Liu, H. Intelligent fault diagnosis of partial deep transfer based on multi-representation structural intraclass compact and double-aligned domain adaptation. Mech. Syst. Signal Process. 197, 110412 (2023).
Google Scholar
Jin, X., Que, Z., Sun, Y., Guo, Y. & Qiao, W. A data-driven approach for bearing fault prognostics. IEEE Trans. Ind. Appl. 55(4), 3394–3401 (2019).
Google Scholar
Wang, H., Yang, J., Wang, R. & Shi, L. Remaining useful life prediction of bearings based on convolution attention mechanism and temporal convolution network. IEEE Access 11, 24407–24419 (2023).
Google Scholar
Xu, G., Hou, D., Qi, H. & Bo, L. High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life. Mech. Syst. Signal Process. 146, 107050 (2021).
Google Scholar
Qin, Y. et al. Dynamic weighted federated remaining useful life prediction approach for rotating machinery. Mech. Syst. Signal Process. 202, 110688 (2023).
Google Scholar
Alfarizi, M. G., Tajiani, B., Vatn, J. & Yin, S. Optimized random forest model for remaining useful life prediction of experimental bearings. IEEE Trans. Ind. Inf. 19(6), 7771–7779. (2023).
Google Scholar
Teoh, Y. K., Gill, S. S. & Parlikad, A. K. Iot and fog computing based predictive maintenance model for effective asset management in industry 4.0 using machine learning. IEEE Internet Things J. 10, 2087 (2021).
Google Scholar
He, C. et al. Real-time fault diagnosis of motor bearing via improved cyclostationary analysis implemented onto edge computing system. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Wan, W., Chen, J. & Xie, J. Mim-graph: A multi-sensor network approach for fault diagnosis of hsr bogie bearings at the iot edge via mutual information maximization. ISA Trans. 139, 574 (2023).
Google Scholar
Liu, J., Ma, C., Gui, H. & Wang, S. Intelligent digital-twin prediction and reverse control system architecture for thermal errors enabled by deep learning and cloud-edge computing. Expert Syst. Appl. 225, 120122 (2023).
Google Scholar
Zhu, X. et al. Deep reinforcement learning-based edge computing offloading algorithm for software-defined iot. Comput. Netw. 235, 110006 (2023).
Google Scholar
Bengherbia, B. et al. Design and hardware implementation of an intelligent industrial iot edge device for bearing monitoring and fault diagnosis. Arab. J. Sci. Eng. 1, 1–17 (2023).
Maurya, M., Panigrahi, I., Dash, D. & Malla, C. Intelligent fault diagnostic system for rotating machinery based on iot with cloud computing and artificial intelligence techniques: A review. Soft Comput. 1, 1–18 (2023).
Google Scholar
Nan, Y., Jiang, S. & Li, M. Large-scale video analytics with cloud-edge collaborative continuous learning. ACM Trans. Sens. Netw. 20, 1 (2023).
Google Scholar
Lu, S., Lu, J., An, K., Wang, X. & He, Q. Edge computing on iot for machine signal processing and fault diagnosis: A review. IEEE Internet Things J. 10, 11093 (2023).
Google Scholar
Fu, L. et al. Edgecog: A real-time bearing fault diagnosis system based on lightweight edge computing. IEEE Trans. Instrum. Meas. 1, 1 (2023).
Google Scholar
Yin, Y., Liu, Z., Zuo, M., Zhou, Z. & Zhang, J. A three-dimensional vibration data compression method for rolling bearing condition monitoring. IEEE Trans. Instrum. Meas. 72, 1–10 (2023).
Google Scholar
Qizhao, W., Li, Q., Wang, K., Wang, H. & Peng, Z. Efficient federated learning for fault diagnosis in industrial cloud-edge computing. Comput. Arch. Inform. Numer. Comput. 103(10), 2319–2337 (2021).
Google Scholar
Goyal, V., Das, R. & Bertacco, V. Hardware-friendly user-specific machine learning for edge devices. ACM Trans. Embedded Comput. Syst. 21(5), 1–29 (2022).
Google Scholar
Li, J., Wang, Y., Zi, Y., Zhang, H. & Wan, Z. Causal disentanglement: A generalized bearing fault diagnostic framework in continuous degradation mode. IEEE Trans. Neural Netw. Learn. Syst. 34, 6250 (2021).
Google Scholar
Li, H., Liu, T., Wu, X. & Li, S. Correlated svd and its application in bearing fault diagnosis. IEEE Trans. Neural Netw. Learn. Syst. 1, 1 (2021).
Google Scholar
Chen, Z. et al. Explainable deep ensemble model for bearing fault diagnosis under variable conditions. IEEE Sens. J. 23, 17737 (2023).
Google Scholar
Choudhary, A., Mian, T., Fatima, S. & Panigrahi, B. Passive thermography based bearing fault diagnosis using transfer learning with varying working conditions. IEEE Sens. J. 23(5), 4628–4637 (2022).
Google Scholar
Yu, X. et al. An adaptive domain adaptation method for rolling bearings’ fault diagnosis fusing deep convolution and self-attention networks. IEEE Trans. Instrum. Meas. 72, 1–14 (2023).
Zhou, Y., Dong, Y. & Tang, G. Time-varying online transfer learning for intelligent bearing fault diagnosis with incomplete unlabeled target data. IEEE Trans. Ind. Inform. 19, 7733 (2022).
Google Scholar
Ruan, D., Han, J., Yan, J. & Gühmann, C. Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction. Sci. Rep. 13(1), 5484 (2023).
Google Scholar
Pilarski, S., Staniszewski, M., Bryan, M., Villeneuve, F. & Varró, D. Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime: For multi-disciplinary design and operation of gas turbines. Softw. Syst. Model. 20, 685–709 (2021).
Google Scholar
Zhang, W., Chen, D., Xiao, Y. & Yin, H. Semi-supervised contrast learning based on multi-scale attention and multi-target contrast learning for bearing fault diagnosis. IEEE Trans. Ind. Inform. 19, 10056 (2023).
Google Scholar
Chen, X. et al. Deep transfer learning for bearing fault diagnosis: A systematic review since 2016. IEEE Trans. Instrum. Meas. 72, 1 (2023).
Google Scholar
Elsamanty, M., Ibrahim, A. & Salman, W. S. Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data. Mech. Syst. Signal Process. 200, 110559 (2023).
Google Scholar
Peng, C., Ouyang, Y., Gui, W., Li, C. & Tang, Z. A multi-indicator fusion-based approach for fault feature selection and classification of rolling bearings. IEEE Trans. Ind. Inform. 19, 8635 (2022).
Google Scholar
Chen, Z., Wu, J., Deng, C., Wang, X. & Wang, Y. Deep attention relation network: A zero-shot learning method for bearing fault diagnosis under unknown domains. IEEE Trans. Reliab. 72(1), 79–89 (2022).
Google Scholar
Yu, G. et al. Few-shot fault diagnosis method of rotating machinery using novel mcgm based cnn. IEEE Trans. Ind. Inform. 19, 10944 (2023).
Google Scholar
Du, W., Hu, P., Wang, H., Gong, X. & Wang, S. Fault diagnosis of rotating machinery based on 1d–2d joint convolution neural network. IEEE Trans. Ind. Electron. 70(5), 5277–5285 (2022).
Google Scholar
Mario, B., Mezhuyev, V. & Tschandl, M. Predictive maintenance for railway domain: A systematic literature review. IEEE Eng. Manag. Rev. 51, 120 (2023).
Google Scholar
Alenizi, F. A., Abbasi, S., Mohammed, A. H. & Rahmani, A. M. The artificial intelligence technologies in industry 4.0: A taxonomy, approaches, and future directions. Comput. Ind. Eng. 1, 109662 (2023).
Google Scholar
Jieyang, P. et al. A systematic review of data-driven approaches to fault diagnosis and early warning. J. Intell. Manuf. 1, 1–28 (2022).
Chen, C., Fu, H., Zheng, Y., Tao, F. & Liu, Y. The advance of digital twin for predictive maintenance: The role and function of machine learning. J. Manuf. Syst. 71, 581–594 (2023).
Google Scholar
CaseWestern Reserve University (CWRU). Bearing Data Center. (Case School of Engineering, CWRU).
Lessmeier, C., Kimotho, J. K., Zimmer, D. & Sextro, W. 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 PHM Society European Conference, vol. 3 (2016)
Yaguo, L. et al. Xjtu-sy rolling element bearing accelerated life test datasets: A tutorial. J. Mech. Eng. 55(16), 1–6 (2019).
Google Scholar
Zhang, P. Vibration time-frequency images of planetary gearboxes. IEEE Dataport 1, 1. (2022).
Google Scholar
Lee, J., Qiu, H., Yu, G. & Lin, J. Bearing Data Set. NASA Prognostics Data Repository. (2007).
Shao, S., McAleer, S., Yan, R. & Baldi, P. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans. Ind. Inf. 15(4), 2446–2455. (2019).
Google Scholar
Liu, S. et al. Bearing fault diagnosis based on improved convolutional deep belief network. Appl. Sci. 10(18), 359. (2020).
Google Scholar
Liu, X., Sun, W., Li, H., Hussain, Z. & Liu, A. The method of rolling bearing fault diagnosis based on multi-domain supervised learning of convolution neural network. Energies 15(13), 614. (2022).
Google Scholar
Nectoux, P. et al. Pronostia: An experimental platform for bearings accelarated life test. In Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, USA, vol. 20 (2012).
Zhao, Z. et al. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Trans. 107, 224 (2020).
Google Scholar
Liu, W., Liu, Y., Li, S. & Chen, W. Adaptive time-reassigned synchrosqueezing transform for bearing fault diagnosis. IEEE Sens. J. 23(8), 8545 (2023).
Google Scholar
Ren, Z., Jiang, Y., Yang, X., Tang, Y. & Zhang, W. Learnable faster kernel-pca for nonlinear fault detection: Deep autoencoder-based realization. J. Ind. Inf. Integr. 40, 100622 (2024).
Jiang, Y., Yin, S. & Kaynak, O. Optimized design of parity relation-based residual generator for fault detection: Data-driven approaches. IEEE Trans. Ind. Inf. 17(2), 1449–1458 (2020).
Google Scholar
Mahesh, T. et al. Data-driven intelligent condition adaptation of feature extraction for bearing fault detection using deep responsible active learning. IEEE Access 1, 1 (2024).
Google Scholar
Lei, Y. et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech. Syst. Signal Process. 138, 106587. (2020).
Google Scholar
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