[2A2] Composite health index construction for online monitoring based on control chart performance optimisation
Y Wang, D Wang and B Hou
Shanghai Jiao Tong University, China
Multiple sensors provide insightful information for machine condition monitoring (MCM) and one mainstream method to comprehensively analyse high-dimensional sensor data is to construct a composite health index (CHI). Considering the difficulty in obtaining degraded data in practical production, this article proposes an optimal CHI based on control chart performance optimisation for online monitoring. The optimal CHI minimises the average run length (ARL) of the control chart when a machine is in degraded conditions, facilitating the prompt detection of first prediction time (FPT). The effectiveness of the proposed method is validated by a turbofan engine dataset. Results show that the optimal CHI can not only detect the FPT in an early degraded stage but also reveal informative sensors related to the degraded module.
