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

Changfan Zhang, Xiang Cheng, Jianhua Liu, Jing He, Guangwei Liu
2018 Journal of Control Science and Engineering  
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
more » ... s 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.
doi:10.1155/2018/8676387 fatcat:qf65eeb6hbcqnaamvpkyb6ltiu