Fault detection and isolation with Interval Principal Component Analysis

Diagnosis method based on Principal Component Analysis (PCA) has been widely developed. However, this method deals only with data which are described by single-valued variables. The purpose of the present paper is to generalize the diagnosis method to interval PCA. The fault detection is performed using the new indicator [SPE]. To identify the faulty variables, this work proposes a new method based on the reconstruction principle to this indicator. It aims to solve an interval linear system to obtain the reconstructed variables. The analysis of the reconstruction conditions permits to determine the useful directions. Then, the residuals structuring according to these directions allows to identify the set of faulty variables. This new diagnosis method based on interval PCA model is validated by a simulation example.