An intelligent extraction method of the full life health indicator of rolling bearings based on one-dimensional deep convolutional neural network and principal component analysis

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (8) : 143-149.

LUO Peng1,2,HU Niaoqing1,2,SHEN Guoji1,2,CHENG Zhe1,2,ZHOU Zijun1,2

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Abstract

The core of rolling bearing fault prognosis methods lies in the construction of a health indicator (HI). Most of the proposed HI is constructed artificially based on expert experience and it can only be applied to the trend analysis of a specific degradation stage of components. To solve the above problems, combined with the one-dimensional characteristics of vibration signals, a full life health indicator (FLHI) of rolling bearing intelligent extraction method based on one-dimensional deep convolutional neural network (1DDCNN) and principal component analysis (PCA) was proposed. 1DDCNN was used to extract features adaptively from the original signals, and it can deeply mine the degradation feature matrix that can represent the health state of the research object. And then, the extracted feature matrix was fused by the PCA method, so as to realize the FLHI intelligent extraction. The experimental results show that FLHI is more advantageous in terms of tendency, robustness, and monotonicity than the traditional HI.

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one-dimensional deep convolutional neural network(1DDCNN) / principal component analysis(PCA) / full life health indicator(FLHI) / intelligent extraction

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LUO Peng1,2,HU Niaoqing1,2,SHEN Guoji1,2,CHENG Zhe1,2,ZHOU Zijun1,2. An intelligent extraction method of the full life health indicator of rolling bearings based on one-dimensional deep convolutional neural network and principal component analysis[J]. Journal of Vibration and Shock, 2021, 40(8): 143-149

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