A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status
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 datadoi:10.1155/2018/8676387 fatcat:qf65eeb6hbcqnaamvpkyb6ltiu