Signal interpretation for monitoring and diagnosis, a coolingsystem testbed

This paper discusses a method for fault detection and isolation (FDI) in continuous dynamic systems. A key aspect of this approach is the coupling of a qualitative diagnosis engine and a monitoring system that computes symbolic feature values through a signal-to-symbol transformation on the continuously sampled measurement data. Signal analysis techniques with a sound statistical basis are employed to generate reliable symbolic data. The methodology is evaluated on the diagnosis of engineered faults in the cooling system of an automobile engine that has been instrumented with temperature and pressure sensors. Results show the interdependency between modeling for diagnosis and the feature extraction system