Fault Diagnosis Based on Non-Negative Sparse Constrained Deep Neural Networks and Dempster–Shafer Theory

Abstract

Fault diagnosis is an important technology to ensure the safe and reliable operation of equipment. Deep learning driven by big data brings new opportunities for fault diagnosis. Due to the diversity and complexity of the actual fault data distribution, a fault diagnosis algorithm based on non-negative sparse constrained deep neural networks (NSCDNN) and Dempster-Shafer theory (DST) is proposed in this paper. The deep neural network is trained by non-negative constraint and sparse constraint, which can learn part-based representation of fault data. The improved DST is combined with the classification confidence and accuracy of NSCDNN model, which can deal with the uncertainty of information from different sensors. Experimental results of the data provided by Case Western Reserve University Bearing Data Center show that the proposed NSCDNN-DST algorithm can improve the accuracy of fault diagnosis effectively.

Keywords

• Fault diagnosis
• deep learning
• sparse autoencoders
• Dempster–Shafer theory
• sensors fusion