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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.

shock and vibration

An Improved Dual-Kurtogram-Based <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>T</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

An Improved Dual-Kurtogram-Based T 2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

An Improved Dual-Kurtogram-Based $T^{2}$ Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings