An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Abstract

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 T 2 statistics based on the covariance determinant. An alarm will be triggered when the T 2 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 T 2 control chart, envelope analysis, and empirical mode decomposition.

A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data

“…In summary, many methods have been developed over the years, including those developed in recent years [23][24][25][26][27][28][29][30][31]; however, there is no simple solution for the selection of an effective band filter for the HFRT for real-world applications. Moreover, the application of machine learning techniques in this domain has attracted a significant amount of attention due to their potential to enhance diagnostic accuracy and efficiency.…”

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

“…Various characteristic analysis methods are widely used in bearing fault diagnosis, such as wavelet transform [7], empirical mode decomposition (EMD) [8], spectral kurtosis (SK) [9], envelope analysis [10], singular spectrum analysis (SSA) [11] and their variants. Gu et al [12] proposed a complementary ensemble empirical mode decomposition (CEEMD)-permutation entropy (PE)-CEEMD algorithm to effectively extract the transient components of the signal using the permutation entropy optimized CEEMD.…”