Condition-based maintenance has been developed as a very efficient strategy for guaranteeing multicomponent system performance and preventing unexpected failures. However, there are shortcomings in the existing condition-based maintenance optimization models. First, the existing models do not utilize the accelerated degradation testing (accelerated degradation testing) data obtained at the stage of component development. Second, most of these models assume perfect repair instead of imperfect repair. Third, the degradation models used in these condition-based maintenance models cannot consider the epistemic uncertainty. Motivated by these problems, this paper presents a new condition-based maintenance optimization model for multicomponent systems with imperfect repair. An integrated degradation prediction framework utilizing both ADT data and field data is presented to timely update the parameters in the proposed model. In order to solve the proposed multivariable, nonlinear programming model, a novel genetic algorithm with self-crossover operation and shift-mutation operation is developed. Numerical examples and comparisons are conducted to evaluate the performance of the proposed model. Results show that the proposed model can evaluate the degradation process of components accurately and achieve lower total maintenance cost.

中文翻译：

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不完美修复下多部件系统的状态维修优化__基于RFAD模型


基于状态的维护已成为保证多组件系统性能并防止意外故障的非常有效的策略。然而，现有的基于状态的维护优化模型存在缺陷。首先，现有模型没有利用在组件开发阶段获得的加速退化测试（accelerateddegradationtesting）数据。其次，这些模型中的大多数都假设完美修复而不是不完美修复。第三，这些基于状态的维护模型中使用的退化模型无法考虑认知不确定性。受这些问题的启发，本文针对不完善修复的多部件系统提出了一种新的基于状态的维护优化模型。提出了利用 ADT 数据和现场数据的集成退化预测框架，以及时更新所提出模型中的参数。为了解决所提出的多变量非线性规划模型，开发了一种具有自交叉操作和移位变异操作的新型遗传算法。进行数值示例和比较来评估所提出模型的性能。结果表明，该模型可以准确评估部件的退化过程，并实现较低的总维护成本。