【文件属性】：
文件名称：Fault Diagnosis Based on Deep Learning
文件大小：416KB
文件格式：PDF
更新时间：2020-03-27 02:35:36
fault detection
As representation scheme can severely limit the
window by which the system observes its world, deep learning
for fault diagnosis is put forward in this paper. It is a real time
online scheme that can enhance the accuracy of detection,
classification and prediction, and efficient for incipient faults
that cannot be detected by traditional statistic technology. A
stacked sparse auto encoder is used to learn the deep
architectures of fault data to minimize the loss of information.
Experiment results show that the proposed method not only
improves the divisibility between faults and normal process, but
also exhibits a better performance on the accuracy of fault
classification for the chemical benchmark, Tennessee Eastman
Process (TEP) data.