一种针对间歇过程过渡状态的故障诊断方法

针对间歇过程过渡状态下具有的复杂过程特性，提出一种基于二维动态主成分分析（2DDPCA）的故障诊断方法。该方法将故障信息划分为“批次内”和“批次间”信息，采用变量贡献图方法隔离故障变量，并依据2DDPCA模型支撑区域中故障变量的相关性变化具体分析故障成因。仿真结果验证了该方法的可行性和有效性。

Fault diagnosis during batch process transition

Process transition during start-up，shut-down or product changeover is frequently encountered in chemical industry. Processes are more prone to various malfunctions and unknown disturbances during transitions. Fault detection and diagnosis during process transitions is critical to ensure process safety and production capacity. A novel modeling method，two-dimensional dynamic principal component analysis（2DDPCA），was developed for monitoring batch process transition in author’s previous work. To follow up，a fault diagnosis method was proposed in this paper. Process characteristics changed by faults were decomposed into “within-batch” and “batch-to-batch” information. Based on this extracted information，contribution plot，associated with the change of fault variables correlation in the optimal region of support，can then be used to isolate and diagnose the abnormal process variables. Simulation results showed the feasibility and validity of the proposed method.