数据驱动的可靠性评估与寿命预测研究进展:基于协变量的方法

Data Driven Reliability Assessment and Life-time Prognostics:A Review on Covariate Models

作为保障工业过程可靠性和经济性的重要技术,可靠性评估与寿命预测在过去几十年得到了越来越广泛的关注和长足的发展.在实际应用中,由于难以获取复杂、高可靠性设备失效机理的物理模型,数据驱动的可靠性评估与寿命预测方法成为近年来的主流.同时,自动监测技术和传感器技术的快速发展,使得在工程实践中不仅能够获取系统的退化数据,还能得到大量的系统运行环境监测数据,从而使得数据驱动寿命预测中基于协变量的方法得到了广泛应用.本文根据系统运行环境中协变量数据的不同变化规律,将基于协变量方法的可靠性评估模型分为:固定协变量模型、时变协变量模型和随机协变量模型,并分别讨论了各模型的发展现状.最后,讨论了协变量处理中存在的一些挑战及未来的研究方向.

Reliability assessment and life-time prognostics have been widely concerned and developed fast in the past decades for their importance in industrial processes. Data driven approaches have been popular due to the complexity of failure mechanism about the reliable complex system. With the development of auto-monitoring and sensor technology, it is easy to obtain degradation data and environment with covariates have emerged. In this paper we review information. A large number of methods based on hazard models the state-of-the-art covariate models in the literature. We classify the approaches into three broad types of models, that is, constant models, time-dependent models and stochastic models We systematically discuss these models and approaches and finally highlight future research challenges.