Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

Xihui Chen,Liping Peng,Chengming Luo,Gang Cheng

doi:10.1155/2019/8716979

Xihui Chen, Liping Peng + Show 2 more

Open Access

https://doi.org/10.1155/2019/8716979

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Abstract

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.

Highlights

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and it usually operates under extreme harsh conditions on the long term
This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN)
The SSD developed from Singular spectrum analysis (SSA) is suitable for processing the vibration signal with nonlinear and nonstationary characteristics generated by planetary gear, and the embedding dimension and component grouping segmentation can be determined

Summary

Introduction

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and it usually operates under extreme harsh conditions on the long term. The original vibration signal with nonlinear and nonstationary characteristics is decomposed into a series of SSDCs by SSD and SSDCs including more feature information that can reflect the degradation state of planetary gear. The multiscale information dimension extracted from SSDCs is regarded as fault feature, and the key of the step is to recognize the degradation state of planetary gear. Traditional pattern recognition methods, such as support vector machine (SVM), back-propagation (BP) neural network, and fuzzy clustering [17, 18], have been applied to the state recognition of mechanical equipment Those methods still have some shortcomings, which are mainly reflected in the inability to process multidimension data, poor recognition effect with small training samples, and being easy to fall into the local optimum and overfitting.

Model Building

Experiment Introduction

Experiment Analysis

Findings

Conclusions