Integrating Spatial and Temporal Features for Bearing Fault Diagnosis A Cross-Domain Analysis Using Vibration and Acoustic Data
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Published
Oct 23, 2025
Mert Sehri
Niousha Khalilian Francisco de Assis Boldt Michel Bouchard Patrick Dumond
https://orcid.org/0000-0003-4781-1719
Niousha Khalilian Francisco de Assis Boldt Michel Bouchard Patrick Dumond
Abstract
Bearing failures cause machinery breakdowns, resulting in financial losses due to production downtimes. To address this, accurate bearing condition monitoring is essential. This paper introduces a cross-domain approach to fault diagnosis using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) models, applied to the Case Western Reserve University (CWRU) dataset and the University of Ottawa Rolling-element Dataset- Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS), which contain both artificial and naturally developed bearing faults. The proposed experimental framework assesses the estimators, training and testing them with raw time-domain data from both acoustic and accelerometer signals, enhancing fault detection across various operating conditions. Results demonstrate that the CNN-LSTM model, when combined with statistical preprocessing, outperforms advanced models in both performance, computational time, and stability, particularly when fusing data from multiple sources. This approach shows promise for practical implementations in industrial predictive maintenance, offering a more reliable solution for reducing downtime and improving operational efficiency. Future work will focus on further optimization of the model and minimizing the data required for effective condition monitoring.
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Keywords
Convolutional Neural Networks, Cross Domain, Deep Learning, Fault Detection and Diagnosis, Machine Learning, Condition Monitoring
References
Bayoudh, K. (2024). A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges. Information Fusion, 105, 102217. https://doi.org/10.1016/j.inffus.2023.102217
Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., Correia, L., & J. Tallón-Ballesteros, A. (2023). The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis. In P. García Bringas, H. Pérez García, F. J. Martínez de Pisón, F. Martínez Álvarez, A. Troncoso Lora, Á. Herrero, … E. Corchado (Eds.), 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (pp. 344–353). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42536-3_33
Chen, Z., He, G., Li, J., Liao, Y., Gryllias, K., & Li, W. (2020). Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery. IEEE Transactions on Instrumentation and Measurement, 69(11), 8702–8712. https://doi.org/10.1109/TIM.2020.2995441
Download a Data File | Case School of Engineering | Case Western Reserve University. (2021, August 10). Retrieved January 15, 2024, from Case School of Engineering website: https://engineering.case.edu/bearingdatacenter/download-data-file
Gupta, M., Wadhvani, R., & Rasool, A. (2023). A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-Based Systems, 259, 110070. https://doi.org/10.1016/j.knosys.2022.110070
Han, B., Yang, Z., Zhang, Z., Bao:, H., Wang, J., Liu, Z., & Li, S. (2022). A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity. Measurement, 198, 111131. https://doi.org/10.1016/j.measurement.2022.111131
Hendriks, J., Dumond, P., & Knox, D. A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. https://doi.org/10.1016/j.ymssp.2021.108732
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jha, R. K., & Swami, P. D. (2021). Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines. Applied Acoustics, 182, 108243. https://doi.org/10.1016/j.apacoust.2021.108243
Layeghy, S., & Portmann, M. (2023). Explainable Cross-domain Evaluation of ML-based Network Intrusion Detection Systems. Computers and Electrical Engineering, 108, 108692. https://doi.org/10.1016/j.compeleceng.2023.108692
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278–2324. https://doi.org/10.1109/5.726791
Lee, J., & Su, H. (2025). Rethinking industrial artificial intelligence: A unified foundation framework. International Journal of AI for Materials and Design, 2(2), 56–68. https://doi.org/10.36922/IJAMD025080006
Li, B., Song, P., & Zhao, C. (2024). Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift. Information Fusion, 106, 102290. https://doi.org/10.1016/j.inffus.2024.102290
Li, J., Ye, Z., Gao, J., Meng, Z., Tong, K., & Yu, S. (2024). Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy. Applied Soft Computing, 159, 111620. https://doi.org/10.1016/j.asoc.2024.111620
Li, Zhuorui, Ma, J., Wu, J., Li, X., & Wang, X. (2024). Implicit Discriminator Domain Adversarial Residual Network for Cross Domain Rolling Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 73, 1–11. https://doi.org/10.1109/TIM.2024.3436093
Li, Zixian, Ding, X., Song, Z., Wang, L., Qin, B., & Huang, W. (2024). Digital twin-assisted dual transfer: A novel information-model adaptation method for rolling bearing fault diagnosis. Information Fusion, 106, 102271. https://doi.org/10.1016/j.inffus.2024.102271
Lian, C., Zhao, Y., Shao, J., Sun, T., Dong, F., Ju, Z., … Shan, P. (2024). CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis. Information Fusion, 104, 102162. https://doi.org/10.1016/j.inffus.2023.102162
Lundström, A., & O’Nils, M. (2023). Factory-Based Vibration Data for Bearing-Fault Detection. Data, 8(7), 115. https://doi.org/10.3390/data8070115
McCarter, D. (2023). Mathematical Analysis of Convolutional Neural Networks. Dissertations and Theses. Retrieved from https://red.library.usd.edu/diss-thesis/114
Meng, Z., Zhan, X., Li, J., & Pan, Z. (2018). An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 130, 448–454. https://doi.org/10.1016/j.measurement.2018.08.010
Mirzaeibonehkhater, M., Labbaf-Khaniki, M. A., & Manthouri, M. (2024, December 15). Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism. arXiv. https://doi.org/10.48550/arXiv.2412.11245
Muhammad Ali, P., & Faraj, R. (2014). Data Normalization and Standardization: A Technical Report. https://doi.org/10.13140/RG.2.2.28948.04489
Rauber, T. W., da Silva Loca, A. L., Boldt, F. de A., Rodrigues, A. L., & Varejão, F. M. (2021). An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals. Expert Systems with Applications, 167, 114022. https://doi.org/10.1016/j.eswa.2020.114022
Samanta, B., & Al-balushi, K. R. (2003). ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES. Mechanical Systems and Signal Processing, 17(2), 317–328. https://doi.org/10.1006/mssp.2001.1462
Sehri, M., & Dumond, P. (2023). University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS). 5. https://doi.org/10.17632/y2px5tg92h.5
Sehri, M., Dumond, P., & Bouchard, M. (2023, November 21). Design of a Data Fusion and Deep Transfer Learning Test Rig for Roller Bearings Diagnosis and Prognosis. Presented at the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2023-117264
Sehri, M., Dumond, P., & Bouchard, M. (2023). University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets. Data in Brief, 49, 109327. https://doi.org/10.1016/j.dib.2023.109327
Sehri, M., Ertarğın, M., Orhan, A., Yildirim, Ö., & Dumond, P. (2024). Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion.
Sehri, M., Hua, Z., Boldt, F. de A., & Dumond, P. (2025). Selective embedding for deep learning. Knowledge-Based Systems, 330, 114535. https://doi.org/10.1016/j.knosys.2025.114535
Sehri, M., Varejão, I., Hua, Z., Bonella, V., Santos, A., Boldt, F. de A., … Varejão, F. M. (2025). Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring. International Journal of Prognostics and Health Management, 16(3). https://doi.org/10.36001/ijphm.2025.v16i3.4239
Su, H., & Lee, J. (2024). Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review. International Journal of Prognostics and Health Management, 15(2). https://doi.org/10.36001/ijphm.2024.v15i2.3993
Wan, H., Guo, S., Yin, K., Liang, X., & Lin, Y. (2020). CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction. Knowledge-Based Systems, 191, 105239. https://doi.org/10.1016/j.knosys.2019.105239
Wang, H., Liu, Z., Peng, D., Yang, M., & Qin, Y. (2022). Feature-Level Attention-Guided Multitask CNN for Fault Diagnosis and Working Conditions Identification of Rolling Bearing. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4757–4769. https://doi.org/10.1109/TNNLS.2021.3060494
Xia, K., Huang, J., & Wang, H. (2020). LSTM-CNN Architecture for Human Activity Recognition. IEEE Access, 8, 56855–56866. https://doi.org/10.1109/ACCESS.2020.2982225
Xu, M., Yu, Q., Chen, S., & Lin, J. (2024). Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD. Information, 15(7), 399. https://doi.org/10.3390/info15070399
Yang, X., Wan, C., Zhang, T., & Xiong, Z. (2022). Feature Extraction of Sequence Data Based on LSTM and its Application to Fault Diagnosis of Industrial Process. 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), 693–698. https://doi.org/10.1109/DDCLS55054.2022.9858505
Yin, K., Chen, C., Luo, B., & Deng, J. (2023). A Bearing Fault Feature Cross-Domain Transfer Method Based on Motor Current Signals. IEEE Transactions on Instrumentation and Measurement, 72, 1–12. https://doi.org/10.1109/TIM.2023.3323048
Yu, Y., Karimi, H. R., Shi, P., Peng, R., & Zhao, S. (2024). A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis. Mechanical Systems and Signal Processing, 211, 111194. https://doi.org/10.1016/j.ymssp.2024.111194
Zhao, H., Combes, R. T. des, Zhang, K., & Gordon, G. J. (2019, May 30). On Learning Invariant Representation for Domain Adaptation. arXiv. https://doi.org/10.48550/arXiv.1901.09453
Zhao, Z., Zhang, Q., Yu, X., Sun, C., Wang, S., Yan, R., & Chen, X. (2021). Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study. IEEE Transactions on Instrumentation and Measurement, 70, 1–28. https://doi.org/10.1109/TIM.2021.3116309
Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., Correia, L., & J. Tallón-Ballesteros, A. (2023). The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis. In P. García Bringas, H. Pérez García, F. J. Martínez de Pisón, F. Martínez Álvarez, A. Troncoso Lora, Á. Herrero, … E. Corchado (Eds.), 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (pp. 344–353). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42536-3_33
Chen, Z., He, G., Li, J., Liao, Y., Gryllias, K., & Li, W. (2020). Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery. IEEE Transactions on Instrumentation and Measurement, 69(11), 8702–8712. https://doi.org/10.1109/TIM.2020.2995441
Download a Data File | Case School of Engineering | Case Western Reserve University. (2021, August 10). Retrieved January 15, 2024, from Case School of Engineering website: https://engineering.case.edu/bearingdatacenter/download-data-file
Gupta, M., Wadhvani, R., & Rasool, A. (2023). A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-Based Systems, 259, 110070. https://doi.org/10.1016/j.knosys.2022.110070
Han, B., Yang, Z., Zhang, Z., Bao:, H., Wang, J., Liu, Z., & Li, S. (2022). A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity. Measurement, 198, 111131. https://doi.org/10.1016/j.measurement.2022.111131
Hendriks, J., Dumond, P., & Knox, D. A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. https://doi.org/10.1016/j.ymssp.2021.108732
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jha, R. K., & Swami, P. D. (2021). Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines. Applied Acoustics, 182, 108243. https://doi.org/10.1016/j.apacoust.2021.108243
Layeghy, S., & Portmann, M. (2023). Explainable Cross-domain Evaluation of ML-based Network Intrusion Detection Systems. Computers and Electrical Engineering, 108, 108692. https://doi.org/10.1016/j.compeleceng.2023.108692
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278–2324. https://doi.org/10.1109/5.726791
Lee, J., & Su, H. (2025). Rethinking industrial artificial intelligence: A unified foundation framework. International Journal of AI for Materials and Design, 2(2), 56–68. https://doi.org/10.36922/IJAMD025080006
Li, B., Song, P., & Zhao, C. (2024). Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift. Information Fusion, 106, 102290. https://doi.org/10.1016/j.inffus.2024.102290
Li, J., Ye, Z., Gao, J., Meng, Z., Tong, K., & Yu, S. (2024). Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy. Applied Soft Computing, 159, 111620. https://doi.org/10.1016/j.asoc.2024.111620
Li, Zhuorui, Ma, J., Wu, J., Li, X., & Wang, X. (2024). Implicit Discriminator Domain Adversarial Residual Network for Cross Domain Rolling Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 73, 1–11. https://doi.org/10.1109/TIM.2024.3436093
Li, Zixian, Ding, X., Song, Z., Wang, L., Qin, B., & Huang, W. (2024). Digital twin-assisted dual transfer: A novel information-model adaptation method for rolling bearing fault diagnosis. Information Fusion, 106, 102271. https://doi.org/10.1016/j.inffus.2024.102271
Lian, C., Zhao, Y., Shao, J., Sun, T., Dong, F., Ju, Z., … Shan, P. (2024). CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis. Information Fusion, 104, 102162. https://doi.org/10.1016/j.inffus.2023.102162
Lundström, A., & O’Nils, M. (2023). Factory-Based Vibration Data for Bearing-Fault Detection. Data, 8(7), 115. https://doi.org/10.3390/data8070115
McCarter, D. (2023). Mathematical Analysis of Convolutional Neural Networks. Dissertations and Theses. Retrieved from https://red.library.usd.edu/diss-thesis/114
Meng, Z., Zhan, X., Li, J., & Pan, Z. (2018). An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 130, 448–454. https://doi.org/10.1016/j.measurement.2018.08.010
Mirzaeibonehkhater, M., Labbaf-Khaniki, M. A., & Manthouri, M. (2024, December 15). Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism. arXiv. https://doi.org/10.48550/arXiv.2412.11245
Muhammad Ali, P., & Faraj, R. (2014). Data Normalization and Standardization: A Technical Report. https://doi.org/10.13140/RG.2.2.28948.04489
Rauber, T. W., da Silva Loca, A. L., Boldt, F. de A., Rodrigues, A. L., & Varejão, F. M. (2021). An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals. Expert Systems with Applications, 167, 114022. https://doi.org/10.1016/j.eswa.2020.114022
Samanta, B., & Al-balushi, K. R. (2003). ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES. Mechanical Systems and Signal Processing, 17(2), 317–328. https://doi.org/10.1006/mssp.2001.1462
Sehri, M., & Dumond, P. (2023). University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS). 5. https://doi.org/10.17632/y2px5tg92h.5
Sehri, M., Dumond, P., & Bouchard, M. (2023, November 21). Design of a Data Fusion and Deep Transfer Learning Test Rig for Roller Bearings Diagnosis and Prognosis. Presented at the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2023-117264
Sehri, M., Dumond, P., & Bouchard, M. (2023). University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets. Data in Brief, 49, 109327. https://doi.org/10.1016/j.dib.2023.109327
Sehri, M., Ertarğın, M., Orhan, A., Yildirim, Ö., & Dumond, P. (2024). Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion.
Sehri, M., Hua, Z., Boldt, F. de A., & Dumond, P. (2025). Selective embedding for deep learning. Knowledge-Based Systems, 330, 114535. https://doi.org/10.1016/j.knosys.2025.114535
Sehri, M., Varejão, I., Hua, Z., Bonella, V., Santos, A., Boldt, F. de A., … Varejão, F. M. (2025). Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring. International Journal of Prognostics and Health Management, 16(3). https://doi.org/10.36001/ijphm.2025.v16i3.4239
Su, H., & Lee, J. (2024). Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review. International Journal of Prognostics and Health Management, 15(2). https://doi.org/10.36001/ijphm.2024.v15i2.3993
Wan, H., Guo, S., Yin, K., Liang, X., & Lin, Y. (2020). CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction. Knowledge-Based Systems, 191, 105239. https://doi.org/10.1016/j.knosys.2019.105239
Wang, H., Liu, Z., Peng, D., Yang, M., & Qin, Y. (2022). Feature-Level Attention-Guided Multitask CNN for Fault Diagnosis and Working Conditions Identification of Rolling Bearing. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4757–4769. https://doi.org/10.1109/TNNLS.2021.3060494
Xia, K., Huang, J., & Wang, H. (2020). LSTM-CNN Architecture for Human Activity Recognition. IEEE Access, 8, 56855–56866. https://doi.org/10.1109/ACCESS.2020.2982225
Xu, M., Yu, Q., Chen, S., & Lin, J. (2024). Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD. Information, 15(7), 399. https://doi.org/10.3390/info15070399
Yang, X., Wan, C., Zhang, T., & Xiong, Z. (2022). Feature Extraction of Sequence Data Based on LSTM and its Application to Fault Diagnosis of Industrial Process. 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), 693–698. https://doi.org/10.1109/DDCLS55054.2022.9858505
Yin, K., Chen, C., Luo, B., & Deng, J. (2023). A Bearing Fault Feature Cross-Domain Transfer Method Based on Motor Current Signals. IEEE Transactions on Instrumentation and Measurement, 72, 1–12. https://doi.org/10.1109/TIM.2023.3323048
Yu, Y., Karimi, H. R., Shi, P., Peng, R., & Zhao, S. (2024). A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis. Mechanical Systems and Signal Processing, 211, 111194. https://doi.org/10.1016/j.ymssp.2024.111194
Zhao, H., Combes, R. T. des, Zhang, K., & Gordon, G. J. (2019, May 30). On Learning Invariant Representation for Domain Adaptation. arXiv. https://doi.org/10.48550/arXiv.1901.09453
Zhao, Z., Zhang, Q., Yu, X., Sun, C., Wang, S., Yan, R., & Chen, X. (2021). Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study. IEEE Transactions on Instrumentation and Measurement, 70, 1–28. https://doi.org/10.1109/TIM.2021.3116309
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Technical Papers