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

• Main Authors: Changfan Zhang, Xiang Cheng, Jianhua Liu, Jing He, Guangwei Liu
• Format: Article
• Language: English
• Published: Hindawi Limited 2018-01-01
• Series: Journal of Control Science and Engineering
• Online Access: http://dx.doi.org/10.1155/2018/8676387

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.