Abstract
Predicting the remaining useful life (RUL) of bearings is critical in ensuring rotating machinery’s reliability and maintenance efficiency. Most of the research in this domain focuses on the fault prognosis of bearings without proper investigation of underlying fault feature pattern mining for degradation analysis. This paper investigates the remaining operational lifespan of bearings with an enhanced feature selection strategy and anomaly monitoring of bearing operational data. Specifically, four different models, namely Bi-LSTM, CNN-LSTM, Conv_LSTM, and encoder-decoder LSTM, are utilized to capture complex temporal dependencies and spatial correlations in the bearing sensor data. In the first stage, various feature selection techniques are engaged to select degradation trend monitoring features over time-domain and frequency-domain analysis. Next, anomaly pattern mining techniques are employed to identify abnormal behavior in the data, a crucial input for the subsequent RUL forecasting models. The anomaly patterns are extracted using unsupervised learning methods such as clustering or autoencoders, enabling the detection of early signs of degradation. Subsequently, the RUL forecasting is performed using four deep learning architectures. The performance of the suggested technique is evaluated using a comprehensive dataset of sensor measurements from bearings, which includes the corresponding remaining useful life (RUL) values. The experimental findings demonstrate that the proposed models demonstrate high accuracy in correctly determining the RUL of bearings. This solution offers proactive and cost-effective maintenance procedures by employing advanced deep learning models and anomalous pattern mining techniques, resulting in increased reliability, reduced downtime, and optimized resource allocation.
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References
Unal P et al (2022) Data-driven artificial intelligence and predictive analytics for the maintenance of industrial machinery with hybrid and cognitive digital twins. Technol Appl Big Data Value. https://doi.org/10.1007/978-3-030-78307-5_14
Gawde S, Patil S, Kumar S, Kotecha K (2022) A sco** review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion. Artif Intell Rev 2022:1–54. https://doi.org/10.1007/S10462-022-10243-Z
Chen J, Huang R, Chen Z, Mao W, Li W (2023) Transfer learning algorithms for bearing remaining useful life prediction: a comprehensive review from an industrial application perspective. Mech Syst Signal Process 193:110239. https://doi.org/10.1016/J.YMSSP.2023.110239
Wang Y, Wang Y (2023) A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation. Appl Intell 53(19):22682–22699. https://doi.org/10.1007/S10489-023-04777-0/METRICS
Gawde S, Patil S, Kumar S, Kamat P, Kotecha K, Abraham A (2023) Multi-fault diagnosis of industrial rotating machines using data-driven approach: a review of two decades of research. Eng Appl Artif Intell 123:106139. https://doi.org/10.1016/J.ENGAPPAI.2023.106139
Akcan E, Kaya Y (2023) A new approach for remaining useful life prediction of bearings using 1D-ternary patterns with LSTM. J Braz Soc Mech Sci Eng 45(7):1–16. https://doi.org/10.1007/S40430-023-04309-4/METRICS
Liu Y, Fan K (2023) Roller bearing fault diagnosis using deep transfer learning and adaptive weighting. J Phys Conf Ser 2467(1):012011. https://doi.org/10.1088/1742-6596/2467/1/012011
Liu Y, **ang H, Jiang Z, **ang J (2023) A domain adaption resnet model to detect faults in roller bearings using vibro-acoustic data. Sensors 23(6):3068. https://doi.org/10.3390/S23063068
Sayyad S, Kumar S, Bongale A, Kamat P, Patil S, Kotecha K (2021) Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions. IEEE Access 9:110255–110286. https://doi.org/10.1109/ACCESS.2021.3101284
Zhang Y, Sun J, Zhang J, Shen H, She Y, Chang Y (2023) Health state assessment of bearing with feature enhancement and prediction error compensation strategy. Mech Syst Signal Process 182:109573. https://doi.org/10.1016/J.YMSSP.2022.109573
He D et al (2023) Remaining useful life prediction for train bearing based on ILSTM network with adaptive hyperparameter optimization. Transp Saf Environ. https://doi.org/10.1093/TSE/TDAD021
Lu W, Wang Y, Zhang M, Gu J (2024) Physics guided neural network: remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process. Eng Appl Artif Intell 127:107350. https://doi.org/10.1016/J.ENGAPPAI.2023.107350
Lui YH et al (2021) Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction. J Power Sources 485:229327. https://doi.org/10.1016/J.JPOWSOUR.2020.229327
Karatzinis GD, Apostolikas NA, Boutalis YS, Papakostas GA (2023) Fuzzy cognitive networks in diverse applications using hybrid representative structures. Int J Fuzzy Syst 25(7):2534–2554. https://doi.org/10.1007/S40815-023-01564-4/FIGURES/14
Wang G, **ang J (2021) Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method. Measurement 176:109161. https://doi.org/10.1016/J.MEASUREMENT.2021.109161
Du X, Jia W, Yu P, Shi Y, Gong B (2023) RUL prediction based on GAM–CNN for rotating machinery. J Braz Soc Mech Sci Eng 45(3):1–22. https://doi.org/10.1007/S40430-023-04062-8/METRICS
Lin T, Wang H, Guo X, Wang P, Song L (2023) A novel prediction network for remaining useful life of rotating machinery. Int J Adv Manuf Technol 124(11–12):4009–4018. https://doi.org/10.1007/S00170-021-08351-1/METRICS
Sayyad S, Kumar S, Bongale A, Kotecha K, Selvachandran G, Suganthan PN (2022) Tool wear prediction using long short-term memory variants and hybrid feature selection techniques. Int J Adv Manuf Technol 121(9):6611–6633. https://doi.org/10.1007/S00170-022-09784-Y
Yang X, Zheng Y, Zhang Y, Wong DSH, Yang W (2022) Bearing remaining useful life prediction based on regression shapalet and graph neural network. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2022.3151169
Ren L, Zhao L, Hong S, Zhao S, Wang H, Zhang L (2018) Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6:50587–50598. https://doi.org/10.1109/ACCESS.2018.2858856
Soualhi A, Medjaher K, Zerhouni N (2015) Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Trans Instrum Meas 64(1):52–62. https://doi.org/10.1109/TIM.2014.2330494
Zhu J, Chen N, Shen C (2020) A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mech Syst Signal Process 139:106602. https://doi.org/10.1016/J.YMSSP.2019.106602
Gao T, Li Y, Huang X, Wang C (2021) Data-driven method for predicting remaining useful life of bearing based on bayesian theory. Sensors 21(1):182. https://doi.org/10.3390/S21010182
Vos K, Peng Z, Jenkins C, Shahriar MR, Borghesani P, Wang W (2022) Vibration-based anomaly detection using LSTM/SVM approaches. Mech Syst Signal Process 169:108752. https://doi.org/10.1016/J.YMSSP.2021.108752
Mao W, Shi H, Wang G, Liang X (2022) Unsupervised deep multitask anomaly detection with robust alarm strategy for online evaluation of bearing early fault occurrence. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2022.3200092
Liu Y et al (2022) The detection of generator bearing failures on wind turbines using machine learning based anomaly detection A direction of arrival estimation method based on deep learning The Science of Making Torque from Wind ( The detection of generator bearing failures on wind turbines using machine learning based anomaly detection. J Phy Conf Series 2265:32066. https://doi.org/10.1088/1742-6596/2265/3/032066
Lei Y, Li C, Gao H, Guo L, Liang J, He J (2022) Research on quantitative monitoring method of milling tool wear condition based on multi-source data feature learning and extraction. Global Reliab Progn Health Manag Conf PHM-Yantai. https://doi.org/10.1109/PHM-YANTAI55411.2022.9942215
P Nectoux et al. 2012 PRONOSTIA: An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, PHM’12 Denver Col- orado United States 1–8
He K, Su Z, Tian X, Yu H, Luo M (2022) RUL prediction of wind turbine gearbox bearings based on self-calibration temporal convolutional network. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2022.3143881
**a J, Feng Y, Lu C, Fei C, Xue X (2021) LSTM-based multi-layer self-attention method for remaining useful life estimation of mechanical systems. Eng Fail Anal 125:105385. https://doi.org/10.1016/J.ENGFAILANAL.2021.105385
QRS Fitni, K Ramli, 2020 Implementation of ensemble learning and feature selection for performance improvements in anomaly-based intrusion detection systems. In: Proceedings—2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 118–124 doi: https://doi.org/10.1109/IAICT50021.2020.9172014
Jebadurai IJ, Paulraj GJL, Jebadurai J, Silas S (2022) Experimental analysis of filtering-based feature selection techniques for fetal health classification. Serb J Electr Eng 19(2):207–224. https://doi.org/10.2298/SJEE2202207J
Luo M et al (2021) Combination of feature selection and CatBoost for prediction: the first application to the estimation of aboveground biomass. Forests 12(2):216. https://doi.org/10.3390/F12020216
Elsayed MS, Le-Khac NA, Dev S, Jurcut AD, 2020 Network anomaly detection using LSTM Based autoencoder. Q2SWinet 2020—In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks 37–45 doi: https://doi.org/10.1145/3416013.3426457
Yin C, Zhang S, Wang J, **ong NN (2022) Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Trans Syst Man Cybern Syst 52(1):112–122. https://doi.org/10.1109/TSMC.2020.2968516
Cao Y, Ding Y, Jia M, Tian R (2021) A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliab Eng Syst Saf 215:107813. https://doi.org/10.1016/J.RESS.2021.107813
Heimes FO (2008) Recurrent neural networks for remaining useful life estimation. Int Conf Progn Health Manag. https://doi.org/10.1109/PHM.2008.4711422
Kong Z, ** X, Xu Z, Chen Z (2023) A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction. Reliab Eng Syst Saf 234:109163. https://doi.org/10.1016/J.RESS.2023.109163
F Rayhan et al. 2023 A bi-directional temporal sequence approach for condition monitoring of broken rotor bar in three-phase induction motors. 3rd International Conference on Electrical, Computer and Communication Engineering doi: https://doi.org/10.1109/ECCE57851.2023.10101518
Zhu C et al (2023) State of health prediction for li-ion batteries with end-to-end deep learning. J Energy Storage 65:107218. https://doi.org/10.1016/J.EST.2023.107218
Peng Y, Chen T, **ao F, Zhang S (2023) Remaining useful lifetime prediction methods of proton exchange membrane fuel cell based on convolutional neural network-long short-term memory and convolutional neural network-bidirectional long short-term memory. Fuel Cells 23(1):75–87. https://doi.org/10.1002/FUCE.202200106
Zhang C, Chen P, Jiang F, **e J, Yu T (2023) Fault diagnosis of nuclear power plant based on sparrow search algorithm optimized CNN-LSTM neural network. Energies 16(6):2934. https://doi.org/10.3390/EN16062934
Szarek D, Jabłoński I, Zimroz R, Wyłomańska A (2023) Non-Gaussian feature distribution forecasting based on ConvLSTM neural network and its application to robust machine condition prognosis. Expert Syst Appl 230:120588. https://doi.org/10.1016/J.ESWA.2023.120588
Zhu G, Zhu Z, **ang L, Hu A, Xu Y (2023) Prediction of bearing remaining useful life based on DACN-ConvLSTM model. Measurement 211:112600. https://doi.org/10.1016/J.MEASUREMENT.2023.112600
Zuo T et al (2023) A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings. Reliab Eng Syst Saf 237:109337. https://doi.org/10.1016/J.RESS.2023.109337
Liu Y, Young R, Jafarpour B (2023) Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes. J Process Control 124:166–178. https://doi.org/10.1016/J.JPROCONT.2023.01.015
Dang W et al (2023) An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement. J Energy Storage 59:106469. https://doi.org/10.1016/J.EST.2022.106469
** X, Sun Y, Que Z, Wang Y, Chow TWS (2016) Anomaly detection and fault prognosis for bearings. IEEE Trans Instrum Meas 65(9):2046–2054. https://doi.org/10.1109/TIM.2016.2570398
Li X, Zhang W, Ding Q (2019) Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 182:208–218. https://doi.org/10.1016/J.RESS.2018.11.011
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Pooja Kamat, Satish Kumar, and Rekha Sugandhi. The first draft of the manuscript was written by Pooja Kamat, Satish Kumar, and Rekha Sugandhi, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Kamat, P., Kumar, S. & Sugandhi, R. Vibration-based anomaly pattern mining for remaining useful life (RUL) prediction in bearings. J Braz. Soc. Mech. Sci. Eng. 46, 290 (2024). https://doi.org/10.1007/s40430-024-04872-4
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DOI: https://doi.org/10.1007/s40430-024-04872-4