IJPE 2019
DOI: 10.23940/ijpe.19.03.p18.895901
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Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

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) t… Show more

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Cited by 3 publications
(2 citation statements)
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“…The degradation model is established by making full use of historical data, and the idea of joint estimation based on the state and static parameters of the particle filter is adopted to obtain the automatically updated results at the same time. Ge et al [16] proposed a vibration signal processing method combining WPT and EMD to extract time and frequencydomain features, applied them in the research of mechanical fault diagnosis, and achieved better results. After the original vibration signal is decomposed and demodulated by a certain vibration signal processing method, it becomes simple and intuitive.…”
Section: Related Workmentioning
confidence: 99%
“…The degradation model is established by making full use of historical data, and the idea of joint estimation based on the state and static parameters of the particle filter is adopted to obtain the automatically updated results at the same time. Ge et al [16] proposed a vibration signal processing method combining WPT and EMD to extract time and frequencydomain features, applied them in the research of mechanical fault diagnosis, and achieved better results. After the original vibration signal is decomposed and demodulated by a certain vibration signal processing method, it becomes simple and intuitive.…”
Section: Related Workmentioning
confidence: 99%
“…It adjusts the weights and thresholds of the network according to the error back propagation so that the sum of the system errors is the smallest [12][13]. Kolmogorov's theorem states that the three layers of the BP neural network are sufficient to approximate complex nonlinear systems with arbitrary precision [14][15][16]. Therefore, this paper adopts a three-layer BP neural network.…”
Section: Bp Neural Networkmentioning
confidence: 99%