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Distributed Diagnosis and Simulation Based Residual Generators

Fault diagnosis is becoming increasingly important for many technical systems. This is for example true in automotive vehicles where fault diagnosis is needed due to economic reasons such as efficient repair and fault prevention, and legislations that mainly deal with safety and pollution. The objective for a diagnostic system is to detect and isolate faults in the system. A diagnostic system consists of several specialized parts, for example residual generators, diagnoses calculation, and communication with other systems.

In embedded systems with dozens of electronic control units that individually states local diagnoses, it can be computationally expensive to find which combination of local diagnoses that points at the correct set of faulty components. A distributed method is proposed where local diagnoses are extended using networked information. The extension is done thru the sharing of local conflicts or local diagnoses between the electronic control units. The number of global diagnoses grows with the number of local diagnoses. Therefore, an algorithm is presented that from the local diagnoses calculates the more likely global diagnoses. This restriction to the more likely diagnoses is sometimes appropriate since there are limitations in processing power, memory, and network capacity.

A common approach to design diagnostic systems is to use residual generators, where each residual generator is sensitive to some faults. A method is presented that constructs residual generators from sets of overdetermined model equations, such that simulation can be used to determine if the residual is zero or not. The method thus avoids the need to analytically transform the set of equations into some specific residual generator form. It can also utilize smaller sub sets of equations like minimally overdetermined sets, and it can further take advantage of object-oriented simulation tools.

Jonas Biteus


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Senast uppdaterad: 2021-11-10