Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning.

You-Ren Wang,Can-Fei Sun,Qi Jin,Guo-Dong Sun

doi:10.3390/s19235222

You-Ren Wang, Can-Fei Sun + Show 2 more

Open Access

https://doi.org/10.3390/s19235222

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Abstract

Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.—the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.

Highlights

Modern machinery and equipment are developing rapidly, and tend to be large, complex and centralized
The variational mode decomposition theory shows that the method for decomposing VMD requires a preset number of parameters, a K modal component of the secondary penalty factor α, and produces results with a greater impact [26]
This paper proposes a composite fault diagnosis method that combines improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN)

Summary

Introduction

Modern machinery and equipment are developing rapidly, and tend to be large, complex and centralized. The planetary gearbox for composite fault diagnosis is a current research difficulty. Domestic and foreign scholars have carried out a good deal of research on planetary gearbox composite faults. It is mainly processed from the perspective of fault separation, and spectral analysis is performed based on expert experience, as described below. Liang et al [4] proposed using a two-group sparse low-rank matrix separation method to separate the bearing and gear composite fault signals, and solve the faults for the components. Dhamande et al [5] proposed a composite fault diagnosis method for gears and bearings

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