Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement

Sensors

Yuanzhang Su¹  Zhen Liu¹  Wenjuan Mei¹  Lei Tang² 

[1] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;Southwest Institute of Technical Physics, Chengdu 611731, China;

关键词: prognostic and health management;    extreme learning machine;    soft sensors;   

DOI  :  10.3390/s22062138

来源: DOAJ

【 摘 要 】

Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design.

【 授权许可】

Unknown