Fault Diagnosis Methods Based On Idiotypic Immune Network

Fault diagnosis is to identify and diagnose fault through the mapping from the defective symptoms space to the defective space. However, many complex systems often change dynamically, and their characteristics are not grasped, so some faults have very strong uncertainties. All of these brought us great difficulties for intelligent diagnostic reasoning via obtaining, expressing and using diagnosis knowledge effectively. Idiotypic-Network theory points out that the various cells and molecules are not in a state of isolation in immune-network model. They constitute a dynamic network structure through self-identification, mutual stimulation and their mutual constraints. This phenomenon is quite similar to the interaction between the nodes of large-scale mechanical and electrical equipments. So we expect to design more efficient fault diagnosis methods by studying this dynamic immune network.With the large equipments becoming more and more complicated, data collected from transducer nodes are influence each other, therefore, which lead to diagnostic errors or misdiagnosis when we only depend on matching the analogical data from fault diagnose knowledge-base. However, the definition of analogy and concentration in the artificial immune algorithms has some problems, for example, the methods for calculating affinity and concentration by using entropy of information have some defects. At the same time, when we evaluate antibodies only by antibody's affinity-degree, we can not accurately acquired the influence between antigen and antibody. If we evaluate it by accustomed definition, we can not acquire the information of antibody's affinity-degree.This thesis proposes a simple model which can synthetically reflect the density of network cells (antibody and antigen) and the information of alike-degree between them according to Jerne's Idiotypic-Network theory,this model also consider the characteristic of influence between transducer nodes and utilization advantage of CBR. Experiments show that the model is faster than other immune algorithms in speeding the later rate of convergence of immune algorithms and improveing the rate of fault diagnosis.The core content of this thesis is the design of fault diagnosis system based on idiotypic immune network and its application to fault diagnosis. Firstly, some basic concepts, framework and principles of the biological immune systems are briefly introduced. We then introduce the research content, research status and basic theory of the artificial immunological network. The framework and flow of some classical algorithms of immunological network model and its structure are studied and analyzed. This thesis also summarizes the fault diagnosis technologies and the principle of electrical equipment fault diagnosis. At last, this thesis gives us a simple fault diagnosis model based on Idiotypic Immune Network, and train it by improved formula including affinity calculation, antibody concentration, antibody stimulation and the form of performance of improved antigen and memory antibody in modal space. The simulation experiment about asynchronous electromotor shows the feasibility of the artificial immune-network system proposed above.