Signal Interpretation for Monitoring and Diagnosis, A Cooling System 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

Signal Interpretation for Monitoring and Diagnosis, a Cooling System Testbed

This paper discusses our method for monitoring and diagnosis of dynamic systems. A key aspect in our approach is the interaction between a qualitative diagnosis engine that requires symbolic input data and a monitoring component that performs a signal to symbol transformation on the observed signals. We have applied the methodology to the cooling system of an automobile engine on which we have installed thermocouples and pressure sensors. Faults can be introduced into the cooling system in a controlled manner. We show that a combination of linear approximation techniques and statistical signal processing can provide robust symbolic signal values for the diagnosis algorithm

Signal interpretation for monitoring and diagnosis A cooling system testbed

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