Distributed diagnosis system combining the immune network and learning vector quantization

A distributed diagnosis system combining the immune network (IN) and learning vector quantization (LVQ) is proposed for accurately detecting faulty sensor outputs in control plants. The system has two execution modes, namely, its training mode, where the LVQ extracts a correlation between each two sensors from their outputs when they work properly, and its diagnosis mode, where the LVQ contributes to testing each two sensors using the extracted correlation, and the IN contributes to determining faulty sensors by integrating the local testing results obtained from the LVQ. With the proposed method, faulty sensors, such as age deteriorated ones, which have been difficult to be detected only by checking each sensor output independently, can be specified