Although Bayesian approaches have been utilized in engineering systems for health prognostics, very little work has been done using Bayesian methods for fault prediction of systems under multiple attributes. To address this issue, in this paper a novel multi-attribute Bayesian control chart is presented for predicting failures of hidden-state systems by jointly considering two performance measures of system operation. The system actual status is represented by a three-state multivariate hidden stochastic process with a normal state, an abnormal state, and a failure state. The working states are unobservable and failure state is observable. Based on the built hidden-state model, a fault prediction scheme integrating both system availability and cost objectives is constructed via a multi-attribute Bayesian control chart to monitor and predict impending risks of the operational systems. The Bayesian control chart alarms when the probability of impending risks reaches a certain control limit, which is optimized and determined by a computational algorithm developed in a semi-Markov decision process framework. The proposed fault prediction scheme provides an appearing feature to jointly consider multiple attributes for hidden-state systems. A real case study of mechanical generators is presented and a comparison with other Bayesian and non-Bayesian methods is also given, which demonstrates the effectiveness and superiority of the proposed approach.

尽管贝叶斯方法已用于健康预测的工程系统，但使用贝叶斯方法对多属性下的系统进行故障预测的工作很少。为了解决这个问题，本文提出了一种新颖的多属性贝叶斯控制图，通过联合考虑系统操作的两个性能指标来预测隐藏状态系统的故障。系统的实际状态由具有正常状态、异常状态和故障状态的三态多元隐藏随机过程表示。工作状态是不可观察的，故障状态是可观察的。基于构建的隐藏状态模型，通过多属性贝叶斯控制图构建集成系统可用性和成本目标的故障预测方案，以监控和预测操作系统即将发生的风险。贝叶斯控制图在即将发生风险的概率达到一定的控制极限时发出警报，这是由在半马尔可夫决策过程框架中开发的计算算法进行优化和确定的。所提出的故障预测方案提供了一个出现的特征来联合考虑隐藏状态系统的多个属性。介绍了机械发电机的真实案例研究，并与其他贝叶斯和非贝叶斯方法进行了比较，这证明了所提出方法的有效性和优越性。贝叶斯控制图在即将发生风险的概率达到一定的控制极限时发出警报，这是由在半马尔可夫决策过程框架中开发的计算算法进行优化和确定的。所提出的故障预测方案提供了一个出现的特征来联合考虑隐藏状态系统的多个属性。介绍了机械发电机的真实案例研究，并与其他贝叶斯和非贝叶斯方法进行了比较，这证明了所提出方法的有效性和优越性。贝叶斯控制图在即将发生风险的概率达到一定的控制极限时发出警报，这是由在半马尔可夫决策过程框架中开发的计算算法进行优化和确定的。所提出的故障预测方案提供了一个出现的特征来联合考虑隐藏状态系统的多个属性。介绍了机械发电机的真实案例研究，并与其他贝叶斯和非贝叶斯方法进行了比较，这证明了所提出方法的有效性和优越性。所提出的故障预测方案提供了一个出现的特征来联合考虑隐藏状态系统的多个属性。介绍了机械发电机的真实案例研究，并与其他贝叶斯和非贝叶斯方法进行了比较，这证明了所提出方法的有效性和优越性。所提出的故障预测方案提供了一个出现的特征来联合考虑隐藏状态系统的多个属性。介绍了机械发电机的真实案例研究，并与其他贝叶斯和非贝叶斯方法进行了比较，这证明了所提出方法的有效性和优越性。