ABSTRACT
Due to the complexity and high reliability requirements of aerospace equipment, it’s a significant challenge to establish the efficiency maintenance management in terms of little failure data or even no failure data in the actual operation or storage environment. This paper investigates the issue of realtime reliability and sequential inspection policy for aerospace equipment based on a Wiener process-based degradation model. Firstly, the stochastic characteristics of the equipment’s degradation is described by the Wiener process. The product-to-product variability and the temporal uncertainty of the degradation can be characterized by the random drift parameters. Secondly, the expression of reliability distribution is obtained with close form by use of the first-hitting time theory. An adaptive method is proposed to evaluate the unknown parameters with the optimal smoothing algorithm (RTS) and the Expectation Maximization algorithm (EM). Once the new degradation information is available, the parameters should be updamaintenance operation space based on virtual ted with Bayesian equation. Moreover, the historical information of the same type product can also be integrated in the selection of the initial parameters to ensure the convergence in the iterative updating. Thirdly, a sequential inspection model is discussed to determine the optimal intervals to satisfy the requirements for the real-time reliability at the certain time. Finally, an example of fatigue crack growth in an aerospace aluminum alloy is given to illustrate the validity of the proposed method.
