Research on fault detection method for middle section idler of belt conveyor

doi:10.16265/j.cnki.issn1003-3033.2023.S2.0009

Abstract

Abstract:

In order to timely and accurately determine the safe operation status of the idlers and reduce their fault operation, the vibration signal characteristics of the idlers under normal working conditions, bearing damage, and fracture in the middle section of a mining belt conveyor were studied based on the actual operating conditions. The vibration signals collected were denoised using DWT and decomposed into the sum of several product function (PF) components through LMD. The components with high correlation coefficients were selected for further analysis, and the extracted best features were used as inputs to the BPNN. A BPNN was constructed to classify the operation status of the idlers, and a diagnostic test of the operation status of the idlers was conducted on a mining belt conveyor. The experimental results show that the DWT-LMD-BPNN algorithm can accurately identify the types of idler faults and locate idler faults, achieving advanced warning of idler faults in the middle section of the belt conveyor. The accuracy of detecting idler faults by integrating the DWT-LMD-BPNN method can reach 93%, which can provide a research basis for building an intelligent monitoring platform for belt conveyors in the future.

Key words: idler fault, vibration signal, discrete wavelet transform (DWT), local mean decomposition (LMD), back propagation neural network (BPNN)

CLC Number:

TH222皮带输送机

JING Qinghe, ZHANG Qiliang, WANG Zengren, LIU Dongxing, XIE Miao, MENG Qingshuang. Research on fault detection method for middle section idler of belt conveyor[J]. China Safety Science Journal, 2023, 33(S2): 41-48.

Figures/Tables 12

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2	0	1	0	0	0	1	1
3	0	0	1	0	1	1	0
︙	︙	︙	︙	︙	︙	︙	︙
50	0	0	0	1	1	0	1

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	1	0	0
	1	1	0
	1	0	1
	1	0	0
实际输出	1	0	1
	1	0	0
	1	1	0
	1	0	1
	1	1	0