Overview of Bearing Remaining Useful Life Prediction Method

As one of the key parts of rotating machinery, the accurate prediction of its remaining useful life(RUL) plays an important role in the normal production and personal maintenance safety of workers. Due to the complex and Changeable Working Environment of rolling bearings, there are less reference samples in the same working condition and more in the different working condition, which is characterized by unbalance, incompleteness, no label and noise interference, the difficulty of RUL prediction for rolling bearings is increased. In recent years, with the continuous appearance of machine learning and deep learning technology, a large number of intelligent bearing life prediction methods based on neural network have been proposed, the existing forecasting methods are analyzed and summarized, and the future development trend is prospected.

關鍵字

Remaining Useful Life ； Machine Learning ； Deep Learning ； Neural Network

參考文獻

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Ren L, Cui J, Sun Y, et al. Multi-bearing remaining useful life collaborative prediction: a deep learning approach[J].Journal of Manufacturing Systems, 2017, 43: 248 - 256.

Zhu J, Chen N, Peng W. Estimation of bearing remaining useful life based on multiscale convolutional neural network[J]. IEEE Transactions on Industrial Electronics, 2018, 66 (4) : 3208 - 3216.

Li X, Zhang W, Ding Q.Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction[J].Reliability Engineering & System Safety, 2019, 182: 208 - 218.

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Overview of Bearing Remaining Useful Life Prediction Method

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