Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status. (15th July 2018)

• Record Type: Journal Article
• Authors: Zhang, Changfan; Cheng, Xiang; Liu, Jianhua; He, Jing; Liu, Guangwei
• Other Names: Liu Qiang Academic Editor.
• Is Part Of: Journal of control science and engineering. Volume 2018(2018)
• Publication Date: 2018-07-15
• Subjects: Control theory -- Periodicals; 629.831205
• DOI: 10.1155/2018/8676387
• Languages: English
• ISSNs: 1687-5249

Abstract

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.