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Sensor diagnosis system combining immune network and learning vector quantization
Author(s) -
Kayama Masahiro,
Sugita Yoichi,
Morooka Yasuo
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391170505
Subject(s) - learning vector quantization , quantization (signal processing) , vector quantization , artificial intelligence , fault (geology) , computer science , pattern recognition (psychology) , fault detection and isolation , mode (computer interface) , artificial neural network , engineering , algorithm , biology , paleontology , actuator , operating system
A distributed diagnosis system combining Immune Network (IN) and Learning Vector Quantization (LVQ) for detecting fault sensors accurately in industrial plants is proposed. It has two execution modes, namely, its training mode where LVQ extracts correlation between each two sensors from their outputs when they work properly, and its diagnosis mode, where LVQ contributes to testing each two sensors using the extracted correlation, while IN contributes to determining fault sensors by integrating these local testing results obtained from LVQ. Discussed here is how to improve diagnosis capability of the developed system. It is shown that the thresholds can be determined effectively by the constraint that the hyperregion corresponding to the normal sensor outputs in each quantization vector space is a single region. Diagnosis capability of the developed system is evaluated using a prototype system for detecting fault sensors of a reheating furnace plant. With the proposed method, abnormal sensors, such as aged deteriorated ones, which have been difficult to be detected only by checking each output of sensor independently, are possible to be specified.