A rolling bearing fault diagnosis method based on LCD and permutation entropy

Permutation entropy (PE) is a new method proposed for detecting the randomicity and dynamic changes of time series, which can be used in the fault diagnosis field. However, due to the complexity of mechanical system, the randomicity and dynamic changes of the vibration signal behaves in different scales and it is necessary to analyze the vibration signal with permutation entropy in a multi-scale way. Therefore, a new method of rolling bearing fault diagnosis based on the local characteristic-scale decomposition (LCD) and PE is put forward. Firstly, the LCD method is used to decompose the vibration signal and the ISCs spanning different scales are obtained. Secondly, calculate the permutation entropy of first few ISC components which contain the main fault information and the entropies accordingly are seen as the characteristic vector and input to the neural network ensemble based classifier. Finally, the proposed method is applied to the experimental data and the analysis results show that the proposed approach can achieve the fault diagnosis of rolling bearings effectively.