Subspace-based Fault Detection and Isolation Methods - Application to Vibration Monitoring

Michèle Basseville (1) , Maher Abdelghani (1) , Albert Benveniste (1)

1 SIGMA2 - Signal, models, algorithms

We address the problem of detecting and isolating faults modeled as changes in the eigenstructure of a linear dynamical system. The purpose of the paper is to describe and analyze new fault detection and isolation algorithms, based on recent stochastic subspace-based identification methods and the statistical local approach to the design of detection algorithms. The application to vibration monitoring of mechanical structures and rotating machines is discussed. A conceptual comparison is made with another detection algorithm based on the instrumental variables identification method, and previously proposed by two of the authors.

Mots clés: FAULT DETECTION AND ISOLATION; SUBSPACE-BASED STOCHASTIC IDENTIFICATION METHODS; STATISTICAL LOCAL APPROACH; STRUCTURAL VIBRATION MONITORING

Domaines: Autre [cs.OH]

Michèle Basseville, Maher Abdelghani, Albert Benveniste. Subspace-based Fault Detection and Isolation Methods - Application to Vibration Monitoring. [Research Report] RR-3299, INRIA. 1997. ⟨inria-00073389⟩