Advertisement
Published in:
2006 | OriginalPaper | Chapter
A New Sensor Fault Diagnosis Technique Based Upon Subspace Identification and Residual Filtering
Published in: Computational Intelligence
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This paper presents a new methodology for designing a detection, isolation, and identification scheme for sensor faults in linear time-varying systems. Practically important is that the proposed methodology is constructed on the basis of historical data and does not require a priori information to isolate and identify sensor faults. This is achieved by identifying a state space model and designing a fault isolation and identification filter. To address time-varying process behavior, the state space model and fault reconstruction filter are updated using a two-time-scale approach. Fault identification takes place at a higher frequency than the adaptation of the monitoring scheme. To demonstrate the utility of the new scheme, the paper evaluates its performance using simulations of a LTI system and a chemical process with time-varying parameters and industrial data from a debutanizer and a melter process.