Diagnosis, Diagnosticability Analysis, and Test Point Design for Multiple Faults Based on Multisignal Modeling and Blind Source Separation
提出一种结合多信号建模与盲源分离的多故障诊断策略，利用盲源分离提取独立故障源信号，重构因果关系结构以提升可诊断性，并在冷轧机液压仿真系统上验证了其有效性。
An effective strategy for analyzing and diagnosing multiple faults is developed, based on the concise causality structure obtained by multisignal modeling and the fault source signals extracted by blind source separation (BSS). The key idea is enlightened by the need to handle the redundant test signals and the multiple fault ambiguity groups when applying multisignal modeling for multiple fault diagnosis. Considering that BSS is inherently suitable to extract the independent source information, it is integrated into the multisignal model to reconstruct the causality structure that will have superior diagnosticability. Preliminary study on test point design is also presented in the proposed strategy. The proposed multiple fault diagnosis strategy has been verified on a hydraulic automatic gauge control simulation system in a cold rolling mill. Results show that it can use less test information to effectively diagnose all simulated single and multiple faults.