交叉分段PCA在锅炉故障诊断中的应用

Optimal Operation Method Based on Cross and Piecewise PCA for Industrial Boilers

工业锅炉系统通常被认为是单模态系统,但是当其负荷变化时,系统会呈现出明显的时变和多模态特性,传统的主成分分析(PCA)方法难以实施有效地故障监测.针对这类问题,提出了一种交叉分段PCA故障诊断方法,选择某一参数值作为负荷变化的模态识别值,通过交叉重叠的分段方法建立多个模型进行监测,交叉部分定义为模态过渡过程,在模态过渡过程利用两个模型进行综合判断,提高故障监测的准确度.通过在某实际工业锅炉的应用表明,与传统PCA方法相比,该方法能够显著提高故障诊断的准确度.

An industrial boiler system is usually considered a single-mode system.When the system load changes,time-varying and multimodal characteristics are significantly exhibited,and the traditional PCA method has difficulty effectively monitoring faults.In view of this problem,we present a cross and piecewise PCA method to diagnose faults.The method selects a parameter value as the modal identification value of load variation,then multiple models are built for monitoring by means of cross and overlap segmentation.Crossover parts are defined as the mode transition processes,in which two models are used for comprehensive assessment to improve the accuracy of fault monitoring.The application in a practical industrial boiler shows that the method can significantly improve the accuracy of fault diagnosis compared with the traditional PCA method.