Health Assessment of Complex Systems Based on the Evidential Reasoning Rule With Nonequidistant Data

Health assessment of complex systems is critical for prognostics and health management (PHM), enhancing operational reliability and preventing catastrophic failures. However, in engineering systems, different testing intervals for subsystems complicate the evaluation of the system’s overall health state. Excluding data points with brief testing intervals could lead to a significant loss of critical information and compromise the accuracy of the health assessment. Toward this end, we develop a novel information fusion mechanism for nonequidistant data, introducing a federal evidential reasoning (FER) rule-based health assessment method. First, we divide nonequidistant indicators into corresponding subsystems and define the calculation period and fusion period. On this basis, a corresponding nonequidistant information processing method is proposed. Second, to reduce the effect of nonequidistant data on the assessment results, the nonequidistant information discount coefficient is introduced to quantify the impact on indicator weight and reliability. Moreover, based on the calculation period and fusion period, the predicted health state and original health state can be obtained by the evidential reasoning (ER) rule-based information fusion mechanism, respectively. Finally, a health assessment experiment of the laser inertial measurement unit (LIMU) is conducted. Comparative experiments demonstrate that the proposed FER rule-based model significantly outperforms the traditional ER rule-based model and other similar health assessment methods.