Research Of Machine Unit Complex Fault Diagnosis Technology Based On Artificial Immune System

Due to the high speed and complexity of large-scale rotating machinery, complex fault probability is also constantly increasing. When multiple concurrent faults happen, those different faults are mixed with each other and feature multiple-coupling and ambiguity, they can't be reduplicated by simple linear fault, so it's hard to accurately describe the mathematical model of those faults and it's very difficult to diagnose them through deterministic criteria. Complex fault has the features of complexity, correlation and uncertainty; therefore it is very difficult to diagnose. This paper aims to discuss the problem of accurate complex fault diagnosis of machine unit, by means of artificial immune method combining with other intelligence methods, the accuracy ratio of complex fault diagnosis can be improved, and a feasible way to complex fault diagnosis may be provided through the idea of intelligent integrated. This article analyzes the characteristics of unit complex faults, optimizes feature classification, forms the core algorithm, and based on principle of immune system distribution and diversity, studies the integrated diagnostic technology to more types of non-dimensional immune detectors. Precise diagnosis mechanisms of complex fault are studied further in this paper. In order to break the bottleneck of complex fault diagnosis, the effective diagnosis method will be developed. Those methods can form a solid theoretical foundation and provide strong technical support for artificial immune system application to unit complex faults diagnose, and those methods can help to carry out intelligence and high diagnostic accuracy ratio of industrial units complex fault diagnosis. The main contents of this dissertation are summarized as followings:Investigation and analysis of the existed generating algorithms to fault detector, we find that they have fixed matching rules based on artificial immune algorithm; the threshold problem is currently not well resolved. The default constant threshold has limitations for different faults. The quality of detectors generated by using single matching rule is not very satisfactory, sometime, error detectors will be generated, and it can result in larger detection false. In the same time it will produce a detector set black hole, and lead to larger false rate and omitting rate. In order to guarantee the higher detector rate, the existed algorithm needed larger detector set, but it take long time to generate detector and detect. In this paper, detector-generating algorithm is studied. Based on the form analysis, the detector set can be generated by the technology of detector covering; the algorithm can adaptively match on the threshold in the course of evolution. According to the principle of the diversity and distribution of artificial immune system, the detectors set can be optimized, efficient and fast detector training method and algorithm can be proposed through the principle of the diversity and distribution of artificial immune system. The more efficient detector can reduce black holes and boundary uncertainty, and prevent misdiagnosis and omitting diagnosis.As concurrent multiple faults happen, different fault features mix with each other and feature multiple coupling and ambiguity, the algorithm of characteristics construction of immune programming is proposed. By through of evolution of antibodies set, the optimal antibodies can be obtained and the new features indicator of best recognition ability can be formed, it can solve the problem of insufficient classification capacity. By algorithm training, mature immune detector with best classification ability can be obtained such that the detector further enhances the immune detector classification capability to the complex fault.Considering that the use of neural networks, fuzzy clustering theory, expert systems integration algorithms has complexity and on-line diagnosis can not meet the fast requirements in recent years, through the multiple complex fault participating in the non-ego training space as a new type fault, the dimensionless immune detectors have been generated by defining five kinds of time domain performance index, those detectors can crossly test. Using decision-making systems of the evidence theory, the excellent detector can be derived through multi-information fusion technology, the detector can diagnose complex fault. A kind of integrated diagnosis algorithm with more simplicity, efficiency, rapidity and practice has been proposed, and basing on integration of some class of dimensionless immune detectors, the unit complex fault diagnosis technology has been formed.Using characteristics of the artificial immune system, negative selection algorithm is adopted and the appropriate coding bits are selected based on characteristics of slowly-varying fault, slowly-varying fault feature can be extracted and reduced, unique fault feature can be obtained, and then the integrated diagnosis can be proceed. Experiment has shown the effectiveness of the method to slowly varying complex fault diagnosis.The research results have been applied to fault diagnosis of industrial units. The intelligent diagnosis systems such as the rubber device GY6204 and GY6205 have been used in the industrial area. Those use of fault diagnosis technology in the industrial sector has made a solid foundation for artificial immune system, and the intelligence and high accuracy ratio of complex fault diagnosis can be realized in the field of industrial unit complex fault diagnosis.Through some study in our paper, the especially further research about the use of the artificial immune system to complex fault can not only make a solid foundation in this field, but also help us to discover some new research problems and possible solutions.