Device Status Evaluation Method Based on Deep Learning for PHM Scenarios

Abstract

The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance.

Keywords

• PHM scene
• deep learning
• health status assessment
• neural network