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A novel method of health indicator construction and remaining useful life prediction based on deep learning

Zhan Xianbiao, Liu Zixuan, Yan Hao, Wu Zhenghao, Guo Chiming, Jia Xisheng

Eksploatacja i Niezawodność

2023

Vol. 25, no. 4

art. no. 171374

The construction of health indicators (HI) for traditional deep learning requires human training labels and poor interpretability. This paper proposes an HI construction method based on Stacked Sparse Autoencoder (SSAE) and combines SSAE with Long short-term memory (LSTM) network to predict the remaining useful life (RUL). Extracting features from a single domain may result in insufficient feature extraction and cannot comprehensively reflect the degradation status information of mechanical equipment. In order to solve the problem, this article extracts features from time domain, frequency domain, and time-frequency domain to construct a comprehensive original feature set. Based on monotonicity, trendiness, and robustness, the most sensitive features from the original feature set are selected and put into the SSAE network to construct HI for state partitioning, and then LSTM is used for RUL prediction. By comparing with the existing methods, it is proved that the prediction effect of the proposed method in this paper is satisfied.

Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

Zheng Guoxiao, Sun Weifang, Zhang Hao, Zhou Yuqing, Gao Chen

Eksploatacja i Niezawodność

2021

Vol. 23, no. 4

612--618

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.