Abstract: A real-time and accurate health condition prediction for an inertial platform is essential for cost-effective and timely maintenance planning and scheduling. Due to the fact that the true health condition of the inertial platform cannot be observed directly, it is assumed that the observations of characteristic parameters are available from monitoring, and the characteristic parameters correlate with health condition of the inertial platform. In this article, a health condition prediction system for the inertial platform is established based on belief rule base (BRB), where the characteristic parameters of the inertial platform are used as the inputs of BRB system and the health condition of platform as the output consequence. To overcome the drawbacks of current parameter optimization algorithms for BRB and satisfy real-time prediction, a parameter estimation algorithm is investigated for online updating BRB prediction system based on the expectation maximization (EM) algorithm. When the new input-output information of system operation is available, the model parameter can be updated online. Real-time health condition prediction for the inertial platform system is validated using the established model and the algorithm under investigation. The experimental results show that the proposed method can implement online parameter estimation of health condition prediction for the inertial platform effectively. In addition, compared with offline parameter optimization method, the proposed method can generate better results in terms of prediction accuracy and operating time, and thus has great potential in engineering practice.

Key words: expert systems, health condition, belief rule base, expectation maximization, prediction