Fault detection based on fractional order models: Application to diagnosis of thermal systems

• Diagnosis methods based on fractional order models.
• Proposed methods: generalized parity space and Luenberger diagnosis observer.
• Reduced number of parameter compared to approaches based on integer order models.
• Application to the detection of faults occurring in thermal systems.
• Use of real data from a single-input multi-output thermal testing bench.

The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems.