带式输送机中间段托辊故障检测方法研究

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

摘要/Abstract

摘要：

为及时准确地判断托辊的安全运行状态,减少托辊故障运行,以某矿带式输送机中间段托辊实际运行工况为背景,研究托辊正常工况、轴承损坏和断裂的故障工况振动信号特征,采用离散小波变换(DWT)降噪处理采集的振动信号,通过局部平均分解(LMD)成若干PF分量之和,选取相关系数大的分量作进一步分析,将提取到的最佳特征作为反向传播神经网络(BPNN)输入,构建反向传播神经网络对托辊运行状态分类,并在某矿带式输送机上进行托辊运行状态诊断试验。试验结果表明:通过DWT-LMD-BPNN算法可以准确识别托辊故障类型,并确定托辊故障发生的位置,实现带式输送机中间段托辊故障超前预警,融合DWT-LMD-BPNN方法检测托辊故障的准确度为93%,可为后续搭建带式输送机智能监测平台提供研究基础。

关键词: 托辊故障, 振动信号, 离散小波变换(DWT), 局部平均分解(LMD), 反向传播神经网络(BPNN)

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)

中图分类号:

TH222皮带输送机

井庆贺, 张启良, 王增仁, 刘东星, 谢苗, 孟庆爽. 带式输送机中间段托辊故障检测方法研究[J]. 中国安全科学学报, 2023, 33(S2): 41-48.

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.

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序号	x₁₁	x₂₁	x₃₁	x₃₂	y₁	y₂	y₃
1	1	0	0	0	1	1	1
2	0	1	0	0	0	1	1
3	0	0	1	0	1	1	0
︙	︙	︙	︙	︙	︙	︙	︙
50	0	0	0	1	1	0	1

输出对比	y₁	y₂	y₃
期望输出	1	0	1
	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