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The method for aircraft unit fault diagnosis, which is based on the self‐organising feature map (SOM) artificial neural net and expertise, has been presented in this paper. A fault probability of components, of which an aircraft unit is comprised, is generated by the SOM. The qualitative possibilities that the components of a specific unit may cause a failure were estimated by an expert in natural language. It was converted into numeric value by means of Zadeh's fuzzy logic. Both the fault probability and the expertise value of the specific components have been considered in this method based on SOM too. The technical feasibility of this method has been shown by the example. The method is useful when a model based on the First Principles or a causal model of a diagnostic object is not available. It is also applicable to the fault diagnosis of other complicated electromechanical equipments.

The objective of this paper is to develop an expert system according to the features of the fault diagnosis for modern commercial aircraft, which is based on case based reasoning and fuzzy logic. The development strategy of this kind of expert system is illustrated by the development process of an A340 fault diagnostic expert system. The key technology, which is involved in the system, is discussed, such as the construction of case base, the structure of indexes, the algorithm of retrieval and the management of case base.

Direct maintenance costs (DMC) of commercial aircraft make a significant contribution to an aircraft's cost of ownership. The aim of our research is to find out some methods to reduce DMC. The paper first points out that design and fault diagnosis are the key factors to influence DMC, disregarding factors unique to a particular airline. A new concept of R&M design – maintenance free operating period and fault diagnosis expert system are discussed in this paper, in order to reduce DMC.