Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

doi:10.23940/ijpe.19.03.p18.895901

Abstract

Abstract: The remaining useful life is key to the decision-making of machinery maintenance. The online prediction of remaining useful life has become a very urgent need for mechanical equipment with high reliability requirements. The aim of this paper is to provide a simple and effective method for predicting the remaining life of the machine under the condition of small sample. The Kolmogorov-Smirnov test theory is used to extract the health state feature of the machine. Based on the Long and Short Term Memory (LSTM) theory, an online method of remaining useful life prediction is proposed. The bearing life vibration data verification shows that the Kolmogorov-Smirnov distance is sensitive to the development and expansion of the defects. Furthermore, the proposed method of remaining useful life prediction based on LSTM theory has high prediction accuracy. The technician can then use this method to take appropriate maintenance operations.

Key words: remaining useful life prediction, kolmogorov-smirnov test, long and short term memory, maintenance strategy, small sample

Yang Ge, Lanzhong Guo, and Yan Dou. Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network [J]. Int J Performability Eng, 2019, 15(3): 895-901.

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