Review on Deep Learning Based Fault Diagnosis
Chenglin Wen, Feiya Lü
Abstract
The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order for online estimation. In view of its powerful ability of representation learning, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some necessity and potential trends, integrated innovation, + knowledge and information fusion, are discussed from the view of data preprocessing, network design and decision.