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首页> 外文期刊>Shock and vibration >An Improved Dual-Kurtogram-Based Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings
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An Improved Dual-Kurtogram-Based Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

机译：基于改进的基于双KurtoGram的滚动轴承的状态监测和复合故障诊断控制图

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摘要

Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate statistical process control is established in this study. The core idea of this method is the capability of the dual-kurtogram in extracting subbands. Vibration data under normal conditions are decomposed by the dual-kurtogram into two subbands. Then, the spectral kurtosis (SK) of Subband I and the envelope spectral kurtosis (ESK) of Subband II are formulated to construct a control limit based on kernel density estimation. Similarly, vibration data that need to be monitored are constructed into two subbands by the dual-kurtogram. The SK of Subband I and the ESK of Subband II are calculated to derive statistics based on the covariance determinant. An alarm will be triggered when the statistics exceed the control limit and suitable subbands for square envelope analysis are adopted to obtain the characteristic frequency. Simulation and experimental data are used to verify the feasibility of the proposed method. Results confirm that the proposed method can effectively perform condition monitoring and fault diagnosis. Furthermore, comparison studies show that the proposed method outperforms the traditional control chart, envelope analysis, and empirical mode decomposition.
机译：条件监测和复合故障诊断是确保旋转机械正常运行的关键关键点。本研究建立了一种基于双Kurtogram算法和多变量统计过程控制的基于双Kurtogram算法的病症监测和复合故障诊断的新方法。该方法的核心思想是双Kurtogram在提取子带中的能力。正常条件下的振动数据由双KurtoGram分解为两个子带。然后，配制子带I II的子带I和包络光谱峰值（ESK）的光谱峰峰（SK）以构建基于核密度估计的控制极限。类似地，需要监视的振动数据被双KurtoGram构造成两个子带。计算子带I的SK和子带II的ESK，以基于协方差决定因素导出统计数据。当统计超出控制限制和正方形包络分析的合适子带时，将触发警报以获得特征频率。模拟和实验数据用于验证所提出的方法的可行性。结果证实，所提出的方法可以有效地执行条件监测和故障诊断。此外，比较研究表明，该方法优于传统的控制图，包络分析和经验模式分解。

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