Multi-domain feature-based early detection of bearing faults using MLP classifier on NASA IMS dataset
DOI:
https://doi.org/10.54939/1859-1043.j.mst.106.2025.48-54Keywords:
Bearing faults prediction; NASA IMS; MLP model; Multi-domain features; Predictive maintenance.Abstract
The degradation of bearing components in industrial machinery leads to increased maintenance costs and unexpected operational downtime. This paper presents a novel methodology that integrates multi-domain statistical feature extraction spanning both time-domain and frequency-domain characteristics to enhance the precision of bearing fault detection. A Multi-Layer Perceptron (MLP) model was trained on the NASA IMS Bearing dataset, achieving a classification accuracy of 86.5% across five degradation stages. Experimental results demonstrate that the proposed method outperforms traditional classifiers such as Support Vector Machine (SVM) and Random Forest, particularly in data-scarce environments. Furthermore, the model is well-suited for deployment on resource-constrained embedded diagnostic systems. This approach offers a practical and efficient solution for predictive maintenance, contributing to the reduction of operational costs in industrial applications.
References
[1]. Hart, E., Clarke, B., Nicholas, G., Kazemi Amiri, A., Stirling, J., Carroll, J., Dwyer-Joyce, R., McDonald, A., and Long, H. "A review of wind turbine main bearings: design, operation, modelling, damage mechanisms and fault detection", Wind Energ. Sci., 5, 105–124, (2020), https://doi.org/10.5194/wes-5-105-2020.
[2]. Deepika, C., Taj, K., and Bedar, P. "Automation in production systems: enhancing efficiency and reducing costs in mechanical engineering", Nanotechnology Perceptions, (2024), https://doi.org/10.62441/nano-ntp.vi.3895.
[3]. Zimroz, R., Bartelmus, W., Barszcz, T., and Urbanek, J. "Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings", Mechanical Systems and Signal Processing, 46, 16–27, (2014), https://doi.org/10.1016/j.ymssp.2013.12.020.
[4]. Purarjomandlangrudi, A., Nourbakhsh, G., Ghaemmaghami, H., and Tan, A. "Application of anomaly technique in wind turbine bearing fault detection", 1984–1988, (2014).
[5]. Gu, J., and Huang, M. "Fault diagnosis method for bearing of high-speed train based on multitask deep learning", Shock and Vibration, 1–8, (2020), https://doi.org/10.1155/2020/8840040.
[6]. Buchaiah, S., and Shakya, P. "Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection", Measurement, 188, 110506, (2021), https://doi.org/10.1016/j.measurement.2021.110506.
[7]. El Laithy, M., Wang, L., Harvey, T. J., Vierneusel, B., Correns, M., and Blass, T. "Further understanding of rolling contact fatigue in rolling element bearings – A review", Tribology International, 140, 105849, (2019), https://doi.org/10.1016/j.triboint.2019.105849.
[8]. Saini, M. K., and Aggarwal, A. "Detection and diagnosis of induction motor bearing faults using multi wavelet transform and naive Bayes classifier", Int. Trans. Electr. Energy Syst., 28, (2018), https://doi.org/10.1002/etep.2526.
[9]. Fanning, P. "High-quality bearings reduce downtime and save cost", Eureka, (2025), https://www.eurekamagazine.co.uk/content/technology/high-quality-bearings-reduce-downtime-and-save-cost/.
[10]. Saruhan, H., Sarıdemir, S., Çiçek, A., and Uygur, I. "Vibration analysis of rolling element bearings defects", Journal of Applied Research and Technology, 12, 384–395, (2014), https://doi.org/10.1016/S1665-6423(14)71620-7.
[11]. Nizwan, C. K. E., Ong, S. A., Yusof, M. F. M., and Baharom, M. Z. "A wavelet decomposition analysis of vibration signal for bearing fault detection", IOP Conference Series: Materials Science and Engineering, 50, 012026, (2013), https://doi.org/10.1088/1757-899X/50/1/012026.
[12]. Jakubek, B., Grochalski, K., Rukat, W., and Sokol, H. "Thermovision measurements of rolling bearings", Measurement, 189, 110512, (2021), https://doi.org/10.1016/j.measurement.2021.110512.
[13]. Zhou, S., Lin, L., Chen, C., Pan, W., and Lou, X. "Application of convolutional neural network in motor bearing fault diagnosis", Computational Intelligence and Neuroscience, 2022, 1–11, (2022), https://doi.org/10.1155/2022/9231305.
[14]. Wang, Y., and Cheng, L. "A combination of residual and long-short-term memory network for bearing fault diagnosis based on time-series model analysis", Measurement Science and Technology, 32, (2020), https://doi.org/10.1088/1361-6501/abaa1e.
[15]. Agarap, A. F. "An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification", arXiv, (2017), https://arxiv.org/abs/1712.03541.
[16]. Pham, V.-N., Do, Q.-H., and Tran, L. D.-A. "Using artificial intelligence (AI) for monitoring and diagnosing electric motor faults based on vibration signals", 2024 International Conference on Information Networking (ICOIN), (2024), https://doi.org/10.1109/ICOIN.2024.104567.
[17]. Wang, X., Meng, R., Wang, G., Liu, X., Liu, X., and Lu, D. "The research on fault diagnosis of rolling bearing based on current signal CNN-SVM", Measurement Science and Technology, 34, (2023), https://doi.org/10.1088/1361-6501/acefed.
[18]. Han, T., Zhang, L., Yin, Z., and Tan, A. "Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine", Measurement, 177, 109022, (2021), https://doi.org/10.1016/j.measurement.2021.109022.
[19]. Qiu, H., Lee, J., Lin, J., and Yu, G. "Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics", Journal of Sound and Vibration, 289, 1066–1090, (2006), https://doi.org/10.1016/j.jsv.2005.03.007.
[20]. Teler, K., Skowron, M., and Orłowska-Kowalska, T. "Implementation of MLP-based classifier of current sensor faults in vector-controlled induction motor drive", IEEE Transactions on Industrial Informatics, 20, 4, 5702–5713, (2024), https://doi.org/10.1109/TII.2023.3336348.
