Learning to Balance the Performance and Deterioration of Aging Systems Through Derating
研究了在老化系统中通过降额（降低工作负荷）来平衡性能与退化的策略，提出一种自适应学习最优工作负荷的方法，并用振动数据验证了模型。
A common strategy of extending the lifetime of an aging system is to reduce its workload below the normal operating level, a practice known as derating. While derating can slow the deterioration process, it often comes at the expense of reduced performance. Thus, derating involves a trade-off between performance and deterioration. Central to the optimal derating strategy is the relationship between deterioration and workload, also referred to as the pd-relationship. In practice, however, this relationship is rarely known a priori. We consider the workload optimization when the pd-relationship can be adaptively learned through sequential experimentation, or active learning. We show that the workload not only influences the performance and deterioration but also controls the speed of learning. The decision-maker must therefore account for the complex interplay between performance, deterioration, and information in real time. We formulate this problem as a partially observable Markov decision process and characterize the optimal policy. A key structural insight is that the optimal workload is always less than the myopic load. We further propose an efficient algorithm based on the fast Gauss transform to compute the optimal policies. The model is validated with vibration data and the performance of the optimal policy is compared against several heuristic policies.