An Approach to Model-based Fault Detection in Industrial Measurement Systems with Application to Engine
Sprache des Titels:
An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an
identification strategy for early detection of appearance of a fault. This approach is model-based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach
is also suitable for off-line FD. The framework that combines FD with isolation and correction
(FDIC) is outlined in this paper. The proposed approach is characterised by automatic threshold determination,
ability to analyse local properties of the models, and aggregation of different fault detection statements.
The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques.
At the same time the models are transparent and interpretable. This novel approach is then verified on a
number of real and simulated data sets of car engine test benches
(both gasoline – Alfa Romeo JTS, and diesel – Caterpillar). It is demonstrated that the approach
can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes.
Sprache der Kurzfassung:
Measurement Science and Technology
Anzahl der Seiten:
Notiz zur Publikation:
Authors: Plamen Angelov, Veniero Giglio, Carlos Guardiola, Edwin Lughofer and Jose Lujan