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.
Highlights
Rolling bearings are important components of rotating machinery
Single variable monitoring is far from sufficient under complex working conditions. us, conducting condition monitoring to provide early warning is indispensable to prevent heavy losses from defects, and this task can be accomplished through multivariate statistical process control (MSPC)
Applied to the dual-kurtogram algorithm, and two band-pass filters are designed to obtain the subbands. e spectral kurtosis (SK) value is obtained from Subband I, and the envelope spectral kurtosis (ESK) is obtained from Subband II. e covariance matrix was calculated by two values of each subband. en, T2 statistics are constructed by a covariance matrix and used to calculate the control limit with kernel density estimation (KDE)
Summary
Rolling bearings are important components of rotating machinery. many failures of these bearing-host systems often occurred by bearing faults, and, bearing condition monitoring is crucial for guaranteeing the safe operation of systems. Chen et al [19] designed an improved kurtogram using redundant second-generation wavelet packet transform and correlated kurtosis; this kurtogram can effectively extract fault features from the local frequency band and prevent frequency aliasing In this method, the criterion parameter is determined by calculating the frequency band of the narrowband timedomain signal. SK yields valid results under certain conditions, it fails in some cases, for example, in the presence of a relatively strong, non-Gaussian noise with high peaks or a relatively high repetition rate of fault impulses To solve this problem, Barszcz and Jablonsk [20] proposed protrugram, in contrast with a fast kurtogram, protrugram calculates the kurtosis of the envelope spectrum amplitude to replace SK, effectively locating the best resonance frequency band for high-impact faults.
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