Twofold fuzzy sets in single and multiple fault diagnosis, using information about normal values

This paper proposes a general approach to diagnosis based on fuzzy pattern matching, making use of consistency and inclusion-based indices in the setting of possibility theory. The approach was first developed for binary attributes and single faults. It was then generalized to any kind of attributes (including multidimensional ones). The paper presents a refined representation (where a distinction is made between effects that are possible for sure and effects that are just not impossible, and where information about (ab)normal values is used). Moreover, an extension to multiple-fault diagnosis and to "cascading faults" is outlined.