免疫检测器证据理论集成的机组复合故障诊断

Complex fault diagnosis of machine unit based on evidence theory and immune detector integrated

针对机组复合故障诊断准确率较低的状况,基于免疫机理的人工免疫智能方法,构建对故障比较敏感的无量纲指标免疫检测器.采用自适应调节匹配阈值和从非己空间产生的候选检测器,能有效减少黑洞和边界不清晰.通过免疫编程优化策略获得最佳识别能力的新特征指标.利用证据理论对多类免疫检测器进行集成诊断,提炼出能直接应用于复合故障诊断的优秀无量纲免疫检测器,机组实验结果表明,所得免疫检测器能快速、准确地进行复合故障诊断.

For the condition that lower accuracy exists in complex fault diagnosis of machine unit, an artificial immune intelligent method based on immune mechanism is proposed, dimensionless immune detectors are constructed and complex fault can be detect and diagnosed. Match thresholds are used and candidate detectors from nonself-space are generated, which can effectively reduce the black hole and unclear border. Immune programming is introduced into feature construct of complex fault diagnosis in order to obtain new feature parameter. A few type immune detectors are integrated and diagnosed by using evidential theory, which can directly be applied to complex fault diagnosis. These excellent immune detectors can improve the accuracy of complex fault diagnosis, and the experiment results show that complex fault can be accurately and rapidly diagnosed.