Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

Abstract

This paper compares and analyzes the quantitative diagnosis methods based on Lempel-Ziv complexity for bearing fault, using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for decomposition of vibration signal measured by acceleration sensors, respectively. The kurtosis and entropy indices are also analyzed in order to select the optimum analysis area from the vibration signal. The variation trend of vibration signal Lempel-Ziv complexity of bearing inner race and outer race with the varying fault severity is analyzed and predicted based on the generation mechanism and characteristics of fault vibration signals. Experimental results show that it is suitable for observing the fault growing trend and severity by examining the value based on improved Lempel-Ziv complexity algorithm using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for signal decomposition, using kurtosis indices for optimal analysis area selection, and using the optimal weight coefficients for complexity calculation.

Rolling bearing quality evaluation based on a morphological filter and a Kolmogorov complexity measure

“…10240 / 95.48 = 107.24 (12) So the length of the structural elements is selected as 100. Due to the amplitude of vibration displacement signal is about 1.5 µm, so the height of the structural elements selected as 2 µm.…”

“…As an time domain indicator, which is easy to be obtained, the Kolmogorov complexity measure has been found to be an effective tool for signal analysis and condition assessment in a bearing system. [11][12][13] The Kolmogorov can be used to extract characteristics which are then used to evaluate bearing quality and to trace to sources including roundness, roughness, waviness and other indicators of quality defects. Morphology filter is an efficient signal processing tool which is also realized in the time domain.…”

Identification of Magnetizing Inrush and Internal Short-Circuit Fault Current in V/x-Type Traction Transformer

“…In order to better represent the essential characteristics of dynamic differential current in traction transformer, the concept of entropy in information theory is introduced to further describe the average information quantity of differential current. The entropy can provide useful information of the signal potential dynamic information, whose value is an accurate measure of average uncertainty and complexity of signal change [21]. The entropy ( ) is defined as…”

A New Method for Weak Fault Feature Extraction Based on Improved MED

“…The mechanical failure of bearings may result in financial loss, and even death. Therefore, condition monitoring and fault diagnosis of rolling bearings are critically important for production efficiency and plant safety in modern enterprises [1,2]. When a rolling bearing fault is weak at an early stage or at a low shaft speed, weak fault features are often embedded in background noise.…”