Difference equation based empirical mode decomposition with application to separation enhancement of multi-fault vibration signals

Abstract

Empirical mode decomposition (EMD) has been applied to various applications in signal processing. However, EMD is susceptible to close mode characteristic frequencies and noise, resulting in the problem of mode mixing. The performance of multi-fault detection in gearboxes will be significantly degraded due to mode mixing in the vibration analysis. Hence, this paper presents a new method to address the mode mixing problem in EMD based gearbox multi-fault diagnosis. In this new method, the differential operation is introduced into the decomposition of the intrinsic mode functions. The decomposition ability of close frequency bands can be improved by the differential operation, and hence, the differential EMD can better identify the modes with close characteristic frequencies than its non-differential counterpart. In addition, time synchronous averaging (TSA) is combined with the differential EMD to address the noise issue. Thus, the proposed TAS and differential EMD based method (TDEMD) can solve the mode mixing problem to provide effective multi-fault detection for gearboxes. The TDEMD has been tested experimentally using vibration data collected from a gearbox with concurrent defects on two different gears. Results showed effective detection of gear multiple faults.

Keywords:

• Differential equation
• empirical mode decomposition
• mode separation
• gear dynamic system
• multiple faults

AMS Subject Classifications:

• 39A10
• 37E99
• 93-04

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Natural Sciences Foundation of China (NSFC) [grant number 51505475], National Basic Research Program of China [grant number 2014CB046303], Youth Natural Sciences Foundation of Jiangsu Province [grant number BK20140200] and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).