基于多信号模型和盲源分离的复合故障诊断方法

利用多信号模型可简明表征系统因果关系以及盲源分离算法可提取系统本源信息的特点, 提出一种新颖有效的复合故障诊断方法. 首先, 针对复合故障下多信号模型出现冗余测试和故障模糊组的情况, 应用盲源分离算法实现测点信息的盲分离, 基于盲信号重建多信号模型的因果结构; 其次, 理论分析了该方法对复合故障具有良好的可诊断性. 轧制过程AGC系统的实验结果表明, 所提出方法对双复合故障和部分多复合故障的隔离和定位准确率可达100%.

Compound fault diagnosis method based on multi-signal model and blind source separation

Based on the concise and informative causality structure obtained by a multi-signal model and the source fault information extracted by a blind source separation algorithm, a novel and effective method for diagnosing compound faults is developed. For the redundant testing signals and the multiple fault ambiguity groups when applying the multi-signal model for compound fault diangosis, a blind source separation algorithm is integrated into the multi-signal model to obtain source fault information, and the causality structure of the multi-signal model is then reconstructed. It’s analyzed theoretically that the proposed method has good diagnosticability for compound faults. The results of applying the proposed compound fault diagnosis method to a hydraulic automatic gauge control(AGC) system in a rolling process show that the diagnosis accuracy rate for all simulated double or multiple compound faults can be 100%.