Model-Based Fault Detection in Multi-Sensor Measurement Systems
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings IEEE IS 2004, Varna, Bulgaria
Original Kurzfassung:
In the process and manufacturing industries, there
has been a large push to produce higher quality products, to
reduce product rejection rates, and to satisfy increasingly
forceful safety and environmental regulations. Hence, the
increasing complexity of measurement systems inside modern
industrial processes with a rising amount of actuators and sensors demands automatic fault detection algorithms which can cope with a huge amount of variables and high-frequented dynamic data. Indeed, humans are being able to classify sensor signals by inspecting by-passing data, but this classifications are very time-consuming then and also have deficiencies because of underlying vague expert knowledge consisting of low-dimensional mostly linguistic relationships. In this paper we propose a model-based fault
detection algorithm which is generic in the sense, that any model correctly describing a functional dependency inside a system can be enclosed easily almost without adjusting any thresholds or other essential parameters. This advanced 'residual view' fault detection includes aspects for incorporating sensor inaccuracies and model qualities as well as processing further normalized residuals for obtaining fault probabilities. Validation results
with respect to data coming from engine test benches are included at the end of the paper.
Sprache der Kurzfassung:
Englisch
Erscheinungsmonat:
6
Erscheinungsjahr:
2004
Anzahl der Seiten:
6
Notiz zur Publikation:
Authors: Edwin Lughofer, Erich Peter Klement, Jose Manuel Lujan and Carlos Guardiola
Forschungs-