Abstract
Prognostics and health management (PHM) technology is an extremely important research focus in the field of reliability engineering. The ultimate goal of applying PHM technology is health management. Aiming at nonlinear degradation systems, an adaptive maintenance policy based on prognostic information is proposed herein. First, a nonlinear degradation model with an adaptive updating mechanism is used to predict the remaining useful life (RUL) of the degrading system. Then, based on the predicted RUL distribution, a multi-objective optimization model is established to address the trade-off between operating cost and availability through a constructed decision boundary, instead of the approach used in previous studies, which considers cost as a single indicator. Using this multi-objective optimization model, an adaptive decision criterion is proposed to evaluate the advantages and disadvantages of different replacement policies, in order to determine the optimal replacement time and dynamic condition monitoring (CM) interval of the degrading system. Finally, an example of gyroscope in an inertial navigation system (INS) is used to verify the effectiveness of the proposed method.
Highlights
Owing to the continuous improvements in modern production technology and industrial manufacturing, engineering systems have undergone rapid developments in terms of integration, automation, precision, and intelligence [1]
Health management is the ultimate goal of applying Prognostics and health management (PHM) technology, which is based on prognostic information, for scientifically and reasonably arranging maintenance activities such as inspections, repairs, replacement of key components, and ordering spare parts [10], [11]
Under the PHM technology framework, the primary task of implementing maintenance decision is to obtain prognostic information related to the probability density function (PDF), cumulative distribution function (CDF), and reliability function (RF) of the remaining useful life (RUL)
Summary
Owing to the continuous improvements in modern production technology and industrial manufacturing, engineering systems have undergone rapid developments in terms of integration, automation, precision, and intelligence [1]. Rafiee et al [17] considered the maintenance decision under a generalized mixed shock mode, with the goal of minimizing the expected cost ratio, and optimizing the CM interval of the system These previous methods are based on the periodic inspection policy, wherein replacement activities are performed during each inspection and the CM interval remains fixed. Letot et al [23] proposed an adaptive maintenance policy with the goal of minimizing long-run costs per unit time and formulated a decision criterion to determine if preventive replacement activities need to be performed at the current state of the system. In view of the abovementioned problems, this article proposes an adaptive maintenance policy based on prognostic information for nonlinear degradation system, in order to obtain the optimal dynamic CM interval and preventive replacement time.
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