AI 中文总结
提出多变量统计框架，利用频率响应函数转换的伪脉冲响应超向量和PERMANOVA，解决MIMO过程控制中反馈掩盖故障及多重比较问题，实现高维功能数据的有效故障诊断。
AI 中文摘要
表征复杂的多输入多输出（MIMO）系统面临两个问题：反馈控制器掩盖了故障方差，使得单变量监测失效；以及在多种条件下重复测试会因多重比较问题而虚增假阳性率。本研究提出一个多变量统计框架来解决这些局限性。我们扩展了一个通过频率响应函数（FRFs）识别系统动力学的统计库，将其转换为时域的伪脉冲响应（PIRs）。这种函数表示捕获了系统的完整动态特征，相比传统的静态标量指标提供了更丰富的诊断轮廓。该框架最初为单输入单输出（SISO）系统开发，现通过引入聚合多个PIRs的超向量扩展到MIMO情形，并使用置换多元方差分析（PERMANOVA）进行评估。这种非参数方法处理了MIMO函数数据中固有的高维性和复杂相关结构。我们证明了PIR超向量比单变量分析更强大，且MIMO PERMANOVA方法优于传统的SISO异常检测。通过区分正常运行、在所研究操作范围内被有效补偿的外部热扰动以及标准单变量方法无法检测到的严重参数故障，该框架为特定故障诊断提供了单一、严格的度量（p < 0.001）。
英文摘要
Characterizing complex Multi-Input Multi-Output (MIMO) systems presents two issues: feedback controllers mask fault variance, rendering single-variable monitoring ineffective, and repeated testing across multiple conditions inflates false positive rates due to the multiple comparisons problem. This study proposes a multivariate statistical framework to resolve these limitations. We extend a statistical library that identifies system dynamics via \textit{Frequency Response Functions} (FRFs) by transforming them into time-domain \textit{Pseudo-Impulse Responses} (PIRs). This functional representation captures the complete dynamic signature of the system, offering a richer diagnostic profile than traditional static scalar metrics. The framework, originally developed for SISO systems, is extended to the MIMO case by introducing supervectors that aggregate multiple PIRs, evaluated using \textit{Permutational Multivariate Analysis of Variance} (PERMANOVA). This non-parametric approach handles the high dimensionality and complex correlation structures inherent in MIMO functional data. We demonstrate that the PIR Supervector is more powerful than single-variable analysis, and the MIMO PERMANOVA approach outperforms traditional SISO anomaly detection. By distinguishing between normal operation, external thermal disturbance effectively compensated within the investigated operating range, and severe parametric faults that standard univariate methods miss, the framework provides a single, rigorous metric for specific fault diagnosis ($p < 0.001$).
CommentsAccepted at ICINCO 2026
