基于改进的证据更新工业过程故障诊断研究

Research on industrial process fault diagnosis based on improved evidence updating

工业生产过程的故障成因颇为复杂,一种故障的故障特征可能有多种表现形式,而多种故障又有可能表现出一种故障特征。因此单模型、单因素的故障诊断方法已显其不足。提出了改进的证据更新的动态故障诊断算法,并结合人工智能方法应用到硝酸生产过程故障诊断系统中。该方法通过对模糊神经网络的描述来确定故障诊断的辨识框架,应用新型的模糊推理方法生成诊断证据,诊断证据再基于改进的证据更新规则来实现证据的动态更新,根据结果来进行故障决策,从而解决了故障模式多样性、故障诊断动态性以及故障特征不确定性的问题。经实例验证,该方法的应用可提高故障诊断确诊率。

The causes of failures in industrial production processes are quite complex.The fault characteristics of a fault may be multiple,and multiple faults may exhibit the same fault characteristics.Therefore,the single-factor,single-model fault diagnosis method has been insufficient.This paper proposes an improved evidence-updated dynamic fault diagnosis algorithm,and combines it with the artificial intelligence method to apply to the industrial production process fault diagnosis system.The method determines the identification framework of fault diagnosis by describing the fuzzy neural network,applies the new fuzzy reasoning method to generate diagnostic evidence,and then based on the improved evidence update rule to realize the dynamic update of the diagnostic evidence before and after the acquisition,which will be updated dynamically.As a result,fault decision is made to solve the fault pattern diversity,fault diagnosis dynamics and uncertainty of fault characteristics.The example analysis proves that the method achieves the purpose of improving the diagnosis rate of fault diagnosis.