Abstract Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel–Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.

中文翻译：

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使用精细复合多尺度 Lempel-Ziv 复杂度的旋转机械智能故障识别

摘要 旋转机械故障诊断在预测与健康管理系统中具有重要作用，旨在识别系统故障的根本原因，为预测和维护提供有效信息。最近，Lempel-Ziv 复杂度（LZC）方法已被用于旋转机械的故障诊断。然而，一个实际问题是 LZC 无法解释测量振动信号中固有的多尺度信息。我们首先介绍一种计算信号的多尺度 LZC 的方法。然而，随着比例因子的增加，LZC 值的方差变得更大。为了解决这个实际问题，本文提出了改进的复合多尺度Lempel-Ziv复杂度（RCMLZC）来估计复杂度。我们发现所提出的 RCMLZC 方法在分析三个模拟的噪声和脉冲信号时始终如一地产生更好的性能。基于RCMLZC，设计了一种新颖的智能故障诊断方法来识别旋转机械的各种故障类型。进行对比实验以确认所提出方法的有效性，包括单故障和复合故障工况。实验结果表明，RCMLZC 在从振动信号中提取故障特征方面比多尺度 LZC、多尺度熵和 LZC 更准确，并且 RCMLZC 在识别旋转机械的各种故障类型方面表现最好。进行对比实验以确认所提出方法的有效性，包括单故障和复合故障工况。实验结果表明，RCMLZC 在从振动信号中提取故障特征方面比多尺度 LZC、多尺度熵和 LZC 更准确，并且 RCMLZC 在识别旋转机械的各种故障类型方面表现最好。进行对比实验以确认所提出方法的有效性，包括单故障和复合故障工况。实验结果表明，RCMLZC 在从振动信号中提取故障特征方面比多尺度 LZC、多尺度熵和 LZC 更准确，并且 RCMLZC 在识别旋转机械的各种故障类型方面表现最好。