Enhanced feature extraction for machinery condition monitoring using recurrence plot and quantification measure
Enhanced feature extraction for machinery condition monitoring using recurrence plot and quantification measure
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DOI：
10.1007/s00170-022-10392-z
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发表时间：
2022-11
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通讯作者：
K. Zhou
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作者：
K. Zhou
机械状态监测是实现基于状态的预测性维护、在工业 4.0 革命背景下实现智能制造的关键应用。基于数据关联的机器学习最近已成为机械故障诊断的主流。为了提高机器学习方法的故障诊断性能，从原始时间序列响应信号中提取高质量的故障相关特征是关键。在这项研究中，我们提出了一种基于信号处理的特征提取方法，即所谓的递归图（RP），来评估从发生故障的机械系统收集的振动信号的隐藏动态特性。为了进一步检查 RP 的属性，采用递归量化分析 (RQA) 来定量测量 RP 中的模式。该方法的主要优点在于其处理具有强系统非线性和环境噪声的非平稳信号的出色能力，从而产生与故障条件高度分离的高质量特征。建立了一种基于轮廓的新颖定量指标，以全面评估特征质量，为机械状态监测提供系统指导。使用公开的齿轮和轴承故障数据集进行综合案例研究，以验证所提出的方法。通过与其他基准方法进行比较，还强调了所提出的方法的增强性能。
Machinery condition monitoring is a crucial application to enable condition-based predictive maintenance, realizing smart manufacturing in the context of the Industry 4.0 revolution. Machine learning that is built upon data correlation has recently become mainstream for machinery fault diagnosis. To boost the fault diagnosis performance of the machine learning methods, extracting high-quality fault-related features from raw time-series response signals is the key. In this research, we propose a signal processing-based feature extraction method, the so-called recurrence plot (RP), to evaluate the hidden dynamic characteristics of the vibration signals collected from the machinery system that is subject to fault. To further examine the properties of RP, the recurrence quantification analysis (RQA) is employed to quantitatively measure the patterns in the RP. The main strength of the proposed methodology lies in its excellent capability in handling the nonstationary signals with strong system nonlinearity and ambient noise, thereby yielding high-quality features with a high degree of separation in terms of the fault condition. A novel quantitative metric based on the silhouette is established to thoroughly assess the feature quality, providing systematic guidance for machinery condition monitoring. Comprehensive case studies using publicly accessible gear and bearing fault datasets are carried out to validate the proposed methodology. The enhanced performance of the proposed methodology also is highlighted by comparing it with other benchmark methods.