Artificial Immune Algorithm And Their Application In Fault Diagnosis

Biological immune system is a highly parallel adaptive information learning system, which can identify and remove the antigenic eye winkers in vading the body. This system can learn, remember and adjust adaptively to keep the stabilization inside The body. During recent years, people begin to realize the revelatory significance of the biological immune mechanism to intelligent algorithm. Artificial immune algorithm(AIA) is this kind of new algorithm which is inspired by the biological immune system. This kind of algorithm has been used in many fields, such as machinery study, unconventionality and malfunction diagnosis, simulation of the behavior of robots, control of robots, inbreak detection of networks and etc. It has been a new effective member of the family of intelligent algorithms.The recent research results of AIS are systematically overviewed in this paper and the directions for further study are also provided. This paper also analyses the present fault diagnosis techniques deeply. At the same time, the basic working mechanism of the biological immune system are also analysed in detail, and this paper summarize the main mechanism that the AIS can draw lessons from.During reducing redundant information,to realize higher data compression rate and maintain better data architecture, a new data reduction algorithm is proposed based on the artificial immune network model. As a result, a new effective data reduction and clustering approach is provided for the fault diagnosis of equipment.On the basis of the advantages and disadvantages of the Forrest's algorithm,this paper study the negatives election algorithm and an improved algorithm for generating the detector set is proposed combining the actual needs of the abnormal detection. Through applying this method to transformer fault diagnosis, this paper has made very good result and has proved the validity of this method.Inspired by the clone and mutation mechanism of immune system and research achievements of artificial immune system, a new self-adaptable fault diagnosis approach with continuous learning is investigated. During learning new knowledge the approach can maintain memory for learned knowledge. The validity of the approach is demonstrated by recognizing standard sample.