Improved ensemble local mean decomposition based on cubic trigonometric cardinal spline interpolation and its application for rotating machinery fault diagnosis

Pei Chen,Huanguo Chen,Jun Pan,Wenhua Chen,Xihui Liang,Jianmin Li

doi:10.1177/1687814020941953

Abstract

Ensemble local mean decomposition has been gradually introduced into mechanical vibration signal processing due to its excellent performance in electroencephalogram signal analysis. However, an unsatisfactory problem is that ensemble local mean decomposition cannot effectively process vibration signals of complex mechanical system due to the constraints of moving average. The process of moving average is time-consuming and inaccurate in complex signal analysis. Therefore, an improved ensemble local mean decomposition method called C-ELMD with modified envelope algorithm based on cubic trigonometric cardinal spline interpolation is proposed in this article. First, the shortcomings in sifting process of ensemble local mean decomposition is discussed and, furthermore, advantages and disadvantages of the common interpolation methods adopted to improve ensemble local mean decomposition are compared. Then, cubic trigonometric cardinal spline interpolation is employed to construct the local mean and envelope curves in a more precise way. In addition, the influence of shape-controlling parameter on envelope estimation accuracy in cubic trigonometric cardinal spline interpolation is also discussed in detail to select an optimal shape-controlling parameter. The effectiveness of cubic trigonometric cardinal spline interpolation for improving the accuracy of ensemble local mean decomposition is demonstrated using a synthetic signal. Finally, the proposed cubic trigonometric cardinal spline interpolation is tested to be effective in gear and bearing fault detection and diagnosis.

Highlights

With the development of industrial technology, rotating machinery in modern mechanical systems is becoming more and more complex to satisfy the increasing application requirement.[1]
This article originated from the research on the application and improvement of ensemble local mean decomposition (ELMD) in rotating machinery fault diagnosis
In consideration of the complexity of vibration signals encountered in practical engineering scenarios, vibration signal obtained from a CWCRU bearing test system and a scale wind turbine gearbox was used to verify the effectiveness of C-ELMD

Summary

Introduction

With the development of industrial technology, rotating machinery in modern mechanical systems is becoming more and more complex to satisfy the increasing application requirement.[1]. Ensemble local mean decomposition (ELMD) was proposed by Cheng, which presented better performance than EEMD in fault diagnosis.[29,30] Subsequently, Zhang et al.[31] optimized the parameters of ELMD to further improve the performance of ELMD.

Results

Conclusion