Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach

Abstract

Condition monitoring (CM) is gaining importance under the profound and comprehensive impact of Industry 4.0 on the modern manufacturing industry, which can monitor the health status of machinery, and support its improvement on availability and reliability. With industrial machinery system tends to be increasingly complex and large, its CM signal processing often suffers from large data scale, redundant information, and mismatched sampling rate among heterogeneous sensors. Besides, though deep learning technology exhibits outstanding ability in data analysis, its application in machinery operational status analysis faces challenges posed by the CM sensing data encoding problem, which aims to match the data entry conditions of the deep learning model, and the deep network efficient training problem. In this paper, we present an industrial machinery condition monitoring analysis method combining the relative degree of contribution (RDoC) based feature selection and deep residual network (DRN). A novel extendable RDoC based feature selection strategy is proposed to identify the optimal feature combination with high representational information density. Information contribution of both sensors and signal features are considered respectively in the feature selection process. Then after a delicately designed matrix-encoding operation, the extracted feature combination is regarded as the input of DRN, to support operational status pattern analysis for machinery CM. Experiment studies on a real dataset demonstrate the feasibility and efficiency of this method. The research will provide a valuable reference to achieve effective monitoring of machinery in Industry 4.0 era.