一种改进的解卷积算法及其在滚动轴承复合故障诊断中的应用

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (21) : 140-150.

论文

齐咏生1，樊佶1，李永亭1，高学金2，刘利强1

作者信息 +

An improved deconvolution algorithm and its application in compound fault diagnosis of rolling bearing

QI Yongsheng1, FAN Ji1, LI Yongting1, GAO Xuejin2, LIU Liqiang1

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文章历史 +

摘要

针对滚动轴承复合故障振动信号非平稳、非线性特性且不同类型故障之间相互耦合，使得传统方法对复合故障冲击特征难以提取的问题，提出了一种基于自适应信号稀疏共振分解（ARSSD）和多点峭度最优最小熵解卷积修正（MK-MOMEDA）的故障诊断新方法。使用ARSSD分析故障信号，并定义一个新的复合指标作为目标函数，利用布谷鸟寻优算法（CSA）对高、低品质因子进行优化选择，获得包含瞬态冲击成分的最优低共振分量；计算其多点峭度谱，提取低共振分量中包含的故障冲击周期成分；之后设定适当的周期区间，进行解卷积运算分离不同的故障特征；通过包络解调，分析谱图中突出的故障特征频率进而识别故障类型。实验平台模拟了滚动轴承两种和三种故障的复合情况，并对所提算法进行了验证，结果表明该方法可有效的从复合故障中提取出各类故障特征，实现故障诊断。

Abstract

Aiming at problems of compound fault signals of rolling bearing being non-stationary and nonlinear, and different types faults being coupled with each other to make it difficult to extract impact features of compound faults with the traditional method, a new fault diagnosis method was proposed based on the adaptive resonance-based signal sparse decomposition (ARSSD) and multipoint kurtosis optimal minimum entropy deconvolution adjusted (MKOMEDA).Firstly, ARSSD was adopted to analyze faulty signals, and a new composite index was defined as the objective function.Cuckoo search algorithm (CSA) was used to optimize high quality factors and low ones to obtain the optimal low resonance component containing transient impact components.Secondly, this low resonance component’s multipoint kurtosis spectrum was calculated to extract its fault impact periodic components.Thirdly, after a suitable period interval was set, different fault features were separated using the deconvolution calculation.Finally, using envelope demodulation, prominent fault feature frequencies in the spectrum were analyzed and then to identify fault types.A test platform was used to simulate composite cases of two and three faults of rolling bearing, and verify the proposed algorithm.Results showed that the proposed method can effectively extract various fault features in composite faults, and realize fault diagnosis.

导出引用

齐咏生1，樊佶1，李永亭1，高学金2，刘利强1. 一种改进的解卷积算法及其在滚动轴承复合故障诊断中的应用[J]. 振动与冲击, 2020, 39(21): 140-150

QI Yongsheng1, FAN Ji1, LI Yongting1, GAO Xuejin2, LIU Liqiang1. An improved deconvolution algorithm and its application in compound fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2020, 39(21): 140-150

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