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Safety is of major concern in many autonomous functions in automotive systems and aerospace. In these application areas, it is standard to use fault trees, and a natural question in many modern systems that include sub-systems like diagnosis, fault-tolerant control, and autonomous functions is how to include the performance of these algorithms in a fault tree analysis for safety. Many possibilities exist but here a systematic way is proposed. It is shown both how safety can be analysed and how the interplay between algorithm design in terms of missed detection rate and false alarm rate is included in the fault tree analysis. Examples illustrate analysis of diagnosis system requirement specification and algorithm tuning.

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Abstract-In fault detection and isolation, diagnostic test results are commonly used to compute a set of diagnoses, where each diagnosis points at a set of components which might behave abnormally. In distributed systems consisting of multiple control units, the test results in each unit can be used to compute local diagnoses while all test results in the complete system give the global diagnoses. It is an advantage for both repair and for fault tolerant control to have access to the global diagnoses in each unit since these diagnoses represent all test results in all units. However, when the diagnoses, for example, are to be used to repair a unit, only the components that are used by the unit are of interest. The reason for this is that it is only these components that can have caused the abnormal behavior. However, the global diagnoses might include components from the complete system, and therefore often include components that are superfluous for the unit. Motivated by this observation, a new type of diagnosis is proposed, the condensed diagnosis. Each unit has a unique set of condensed diagnoses which represents the global diagnoses. The benefit of the condensed diagnoses is that they only include components used by the unit while still representing the global diagnoses. The proposed method is applied to an automotive vehicle and the results from the application study show the benefit of using condensed diagnoses compared to global diagnoses.

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