Wavelet domain principal feature analysis for spindle health diagnosis

Abstract

This article introduces a hybrid signal processing technique for spindle health monitoring and diagnosis, through the integration of wavelet packet transform and principal feature analysis. Vibration signals measured from a spindle test system with different defect conditions are first decomposed into multiple sub-frequency bands by means of the wavelet packet transform. Statistical parameters such as energy and Kurtosis of these sub-frequency bands are then calculated. Subsequently, Principal Feature Analysis, which is an extension of the Principal Component Analysis, is performed on the statistical parameters to aid in the selection of the most representative features, which can be distinctively separated from each other, as inputs to a diagnostic classifier. Experimental analysis of sensor data measured from the spindle test system has verified the effectiveness of the developed technique.

Current envelope analysis for defect identification and diagnosis in induction motors

“…6(a and b), which corresponds to the features from motor current and current envelope, respectively. A criterion of 95% is set to select the dominant feature in this study, since it contains the most information of these feature sets [39]. It is seen that 15 features from motor current and 18 features from current envelope are selected, respectively.…”

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

“…Their fault may cause unexpected breakdown of the machine systems and lead to significant economic loss or even personnel casualties [1,2]. Since structural defectcaused vibration signals often reflect changes of the dynamic characteristics related to the gearbox, many researches focus on transient feature extraction of the vibration signal and fault recognition of the defective gearboxes using vibration signal analysis [3]. Nonetheless, a number of factors related to structural transformation, friction, velocity shear, and strike affect the vibration-oriented signal study and reduce the effectiveness of defective diagnosis.…”

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

“…Among commonly used techniques, vibration-based analysis has been widely established to diagnose bearing faults due to the fact structural defects can cause changes of the bearing dynamic characteristics as manifested in vibrations [2]. However, some non-linear factors, such as clearance, friction, and stiffness, affect complexity of the vibration signals.…”

“…It has attracted increasing attention due to its ability in providing more flexible time-frequency decomposition, especially in the high-frequency region. The WPD is widely used in various machine fault diagnosis applications because of its excellent performance [2]. For example, research in [8] used different sets of wavelet packet vectors to represent bearing vibration signals under different defect conditions.…”

“…It is expected to combine these two parameters to enhance the defect severity assessment capability. In practice, if more parameters are used in feature extraction, they do not necessarily improve diagnostic performance but rather increase the computational cost [2,13]. New parameters that can facilitate effective feature extraction are needed.…”