基于贝叶斯网络的水轮发电机组状态检修方法研究

针对现有水轮发电机组状态检修研究大多侧重于故障诊断结果，而忽略了为检修人员提供良好的辅助维修决策的问题。文章引入贝叶斯网络，设计了一种基于传感器诊断策略与贝叶斯网络模型的状态检修方法。该方法采用传感器诊断策略对传感器监测信号进行误报警、冗余报警与潜在故障判断后，通过贝叶斯网络模型对传故障进行有效诊断，并以输出的故障可能性与故障的危险性配合进行风险评估，通过风险评估结果为检修人员对检修过程中故障排查的顺序提供合理的参考。最后，通过精准度分析、受试者工作特征（ROC）曲线、校准曲线进行模型验证，结果表明，系统运行准确度达到80%。

Condition-based maintenance of hydroelectric generating sets based on Bayesian network

A Bayesian network model is adopted in the maintenance of hydroelectric generating sets to provide maintenance personnel with a better tool in decision making of auxiliary maintenance. Based on the sensor diagnosis strategy and a Bayesian network model, we design a condition-based maintenance method that uses the strategy to handle the cases of false alarm, redundant alarm, and potential fault diagnosis on sensor monitoring signals. We apply the Bayesian network model to effective diagnosis of transmission faults, and carry out risk assessment via combining fault probability and fault risk. The results of risk assessment provide reasonable information for the maintenance personnel to set up a procedure for failure checking during maintenance. Finally, we use accuracy analysis to analyze the method and its practical operation, and verify the calibration curve by comparing the modeled results with field data, showing that the operating accuracy of the system is 80%.