Approach of fault diagnosis based on similarity degree matching distance function

A new diagnosis method based on the similarity degree matching distance function is proposed. This method solves the problem that the traditional fault diagnosis methods based on transition system model cannot deal with the "special state" which cannot match the target states completely. For evaluating the relationship between the observation and the target states, this paper first defines a new distance function based on the viewpoint of energy to measure the distance between two attribute values. After that, all the distances of the attributes in the state vector are used to synthesize the distance between two states. For calculating the similarity degree between two states, a trend evaluation method is developed. It analyzes the main direction of the trend of the state transfer according to the distances between the observation and each target state and their historical records. Applying the diagnosis method to a primary power subsystem of a satellite, the simulation result shows that it is effective.