基于电机电流特征分析的输送带错轨故障诊断新方法。

Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis.

机构信息

School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.

Daifuku Airport Technologies, Sutton Road, Hull HU7 0DR, UK.

出版信息

Sensors (Basel). 2023 Mar 31;23(7):3652. doi: 10.3390/s23073652.

DOI:10.3390/s23073652

PMID:37050710

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10099345/

Abstract

For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA.

摘要

本文首次提出、研究和验证了一种基于电机电流特征分析（MCSA）的带式输送机皮带跑偏在线自动诊断新技术。研究了三种诊断技术，通过实验进行了评估和比较，用于输送带跑偏诊断。所提出的技术基于三个高阶谱诊断特征：双相干，三相干和三阶谱模的互相关（CCSM3）。通过综合实验对所提出的技术进行了研究，结果表明，基于 CCSM3 的技术通过 MCSA 对输送带跑偏进行诊断非常有效。