Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

Changfan Zhang,Jianhua Liu,Guangwei Liu,Jing He,Xiang Cheng

doi:10.1155/2018/8676387

Changfan Zhang, Jianhua Liu + Show 3 more

Open Access

https://doi.org/10.1155/2018/8676387

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Abstract

The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

Highlights

Precise diagnosis of the wheel-rail adhesion state is an important prerequisite for adhesion control
Li Ningzhou [5] studied the adhesion feature of the air brake of a locomotive and used the optimized recursive neural network to optimize the parameters of the adhesive controller and improve the utilization rate of locomotive adhesion, thereby obtaining a good experimental result
This paper proposes a sparse autoencoder deep neural network with dropout to diagnose the wheel-rail adhesion state of a locomotive

Summary

Introduction

Precise diagnosis of the wheel-rail adhesion state is an important prerequisite for adhesion control. Li Ningzhou [5] studied the adhesion feature of the air brake of a locomotive and used the optimized recursive neural network to optimize the parameters of the adhesive controller and improve the utilization rate of locomotive adhesion, thereby obtaining a good experimental result These methods are more convenient and intelligent than the general mechanism analysis method. This paper proposes a sparse autoencoder deep neural network with dropout to diagnose the wheel-rail adhesion state of a locomotive. This deep neural network can significantly reduce the adverse effect of overfitting, making the learned features more conducive to classification and identification.

Description of Adhesion Status

Deep Neural Network

Experimental Research and Analysis

36 Epochs

Findings

Conclusions