Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful life estimation or likelihood of failure in near future helps operators to assess the operating conditions and, therefore, provides better opportunities for sound repair and maintenance decisions. However, many operators believe remaining useful life estimation and failure prediction solutions are incomplete answers to the maintenance challenge. They argue that knowing the likelihood of failure in the future is not enough to make maintenance decisions that minimize costs and keep the operators safe. In this paper, we present a maintenance framework based on offline supervised deep reinforcement learning that instead of providing information such as likelihood of failure, suggests actions such as "continuation of the operation" or "the visitation of the repair shop" to the operators in order to maximize the overall profit. Using offline reinforcement learning makes it possible to learn the optimum maintenance policy from historical data without relying on expensive simulators. We demonstrate the application of our solution in a case study using the NASA C-MAPSS dataset.
翻译:近些年来,为解决剩余使用寿命估计和故障预测问题,提出了若干机器学习和深层学习框架,以解决剩余使用寿命估计和故障预测问题。获得剩余使用寿命估计或近期内可能出现的故障,有助于操作者评估操作条件,从而提供更好的机会作出妥善的修理和维护决定。然而,许多操作者认为,其余使用寿命估计和故障预测解决方案对维护挑战的答案不完整。他们认为,了解未来故障的可能性不足以作出维护决定,从而最大限度地降低成本,保护操作者的安全。在本文中,我们提出了一个维护框架,其依据是离线监督的深层强化学习,而不是提供失败可能性等信息,向操作者建议“继续操作”或“修理厂参观”等行动,以便最大限度地增加总利润。利用离线强化学习,可以不依赖昂贵的模拟器,从历史数据中学习最佳维护政策。我们用美国航天局的C-MAPSS数据集在案例研究中展示了我们解决方案的应用情况。