Context-aware machine learning for estimating the remaining useful life of bearings under varying speed operating conditions



Published Oct 26, 2023


Remaining useful life estimation is a crucial and complicated task in predictive maintenance in order to reduce downtime and avoid catastrophic breakdowns in industrial plants. Thanks to the recent advances in our machine learning era, deep learning models can effectively deal with modeling complex phenomena such as the bearing degradation process, specifically under varying operating conditions. However, obtaining large labeled datasets for training the data-dependent deep learning models is challenging and expensive. To overcome this limitation, a phenomenological model has been used in this study as an effective approach to creating synthetic run-to-failure datasets under varying operating conditions. The suggested methodology is able to adjust synthetic run-to-failure datasets to the different periodic speed profiles, including the speed ranges that pass the resonance frequency of the structure. A Context-aware Domain Adversarial Neural Network is proposed to remove the domain shift between the simulated signals and the real ones as well as enable the deep learning model to understand the varying speed operating conditions and the sequential order of the measurements. The simulated signals are used as the source domain and a limited number of the real signals are used as the unlabeled samples for the domain adaptation task. Context awareness is introduced to the network by integrating contextual information into the architecture of the Domain Adversarial Neural Network, leading to an improvement in the model performance and its generalization ability. A dataset captured in a bearing test rig is adopted to verify the proposed method. Results show that context awareness can result in better performance and also more robust predictions against major speed changes in varying speed scenarios compared to the non-context-aware models.

How to Cite

Hosseinli, S. A., Ooijevaar, T., & Gryllias, K. (2023). Context-aware machine learning for estimating the remaining useful life of bearings under varying speed operating conditions. Annual Conference of the PHM Society, 15(1).
Abstract 108 | PDF Downloads 100



machine learning, remaining useful life, bearing, context-aware, transfer learning, condition monitoring

Antoni, J. (2007). Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration, 304(3–5), 497–529.

Buzzoni, M., D’Elia, G., & Cocconcelli, M. (2020). A tool for validating and benchmarking signal processing techniques applied to machine diagnosis. Mechanical Systems and Signal Processing, 139, 106618.

Chen, Y., Peng, G., Xie, C., Zhang, W., Li, C., & Liu, S. (2018). ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis. Neurocomputing, 294, 61–71.

Chi, F., Yang, X., Shao, S., & Zhang, Q. (2022). Bearing Fault Diagnosis for Time-Varying System Using Vibration–Speed Fusion Network Based on Self-Attention and Sparse Feature Extraction. Machines, 10(10), 948.

Ciani, L., Galar, D., & Patrizi, G. (2019). Improving context awareness reliability estimation for a wind turbine using an RBD model. 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 1–6.

Cocconcelli, M., Rubini, R., Zimroz, R., & Bartelmus, W. (2011). Diagnostics of ball bearings in varying-speed motors by means of Artificial Neural Networks. 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011, CM 2011/MFPT 2011, 2, 760 – 771.

Farahani, H. S., Fatehi, A., Nadali, A., & Shoorehdeli, M. A. (2021). Domain Adversarial Neural Network Regression to design transferable soft sensor in a power plant. Computers in Industry, 132, 103489.

Farhat, M. H., Chiementin, X., Chaari, F., Bolaers, F., & Haddar, M. (2021). Digital twin-driven machine learning: ball bearings fault severity classification. Measurement Science and Technology, 32(4), 044006.

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-Adversarial Training of Neural Networks.

Gryllias, K. C., & Antoniadis, I. A. (2012). A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2), 326–344.

Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.

Leturiondo, U., Salgado, O., Ciani, L., Galar, D., & Catelani, M. (2017). Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measurement, 108, 152–162.

Liu, C., & Gryllias, K. (2022). Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis. IEEE Transactions on Industrial Informatics, 18(9), 5760–5770.

Liu, C., Mauricio, A., Qi, J., Peng, D., & Gryllias, K. (2020). Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics.

Liu, J., Cao, H., Su, S., & Chen, X. (2023). Simulation-Driven Subdomain Adaptation Network for bearing fault diagnosis with missing samples. Engineering Applications of Artificial Intelligence, 123.

McFadden, P. D., & Smith, J. D. (1984). Model for the vibration produced by a single-point defect in a rolling element bearing. Journal of Sound and Vibration, 96(1), 69–82.

Ooijevaar, T. H., Pichler, K., Di, Y., Devos, S., Volckaert, B., Hoecke, S. Van, & Hesch, C. (2019). Smart Machine Maintenance Enabled by a Condition Monitoring Living Lab. IFAC-PapersOnLine, 52(15), 376–381.

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.

Rezamand, M., Kordestani, M., Orchard, M. E., Carriveau, R., Ting, D. S.-K., & Saif, M. (2021). Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions. IEEE Transactions on Industrial Informatics, 17(3), 1742–1752.

Salunkhe, V. G., & Desavale, R. G. (2021). An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 4(3).

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need.

Xia, B., Wang, K., Xu, A., Zeng, P., Yang, N., & Li, B. (2022). Intelligent Fault Diagnosis for Bearings of Industrial Robot Joints Under Varying Working Conditions Based on Deep Adversarial Domain Adaptation. IEEE Transactions on Instrumentation and Measurement, 71, 1–13.

Xue, L., Li, N., Lei, Y., & Li, N. (2017). Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions. Materials, 10(6), 675.
Technical Research Papers

Most read articles by the same author(s)