Intelligent Method Research For Fault Diagnosis Of Mechanism Based On Uncertainty Theory

Mechanical fault diagnosis refers to the recognition and diagnosis of fault mechanism,fault causes,fault positions,and fault degree arising from the process operation by resorting to the means of test analysis and the theory of diagnosis.On these bases,maintenance plans and preventive measures could be further determined. Based on the abnormal state of mechanical equipment,mechanical fault diagnosis could realize fault location,make qualitative analysis,and fault reason analysis according to the state recognition.But in general,the mapping relationship between fault symptom and fault reason of the mechanical equipment is not just one to one,but many-to-one and(or) one-to-many because of the complexity of system structure, operation process,and running environment.Moreover,there exist multitudes of uncertainties in fault diagnosis process due to random,fuzzy,or uncertain factors.Traditional methods for making mechanical fault diagnosis such as System Reliability Analysis,Fault Tree Analysis could not solve uncertain problems existing in the mechanical fault diagnosis process.Uncertainty theories and methods developed rapidly in recent years,have apparent advantages in solving uncertainty problems,and become an important research field in mechanical fault diagnosis.The research in this dissertation is supported by the National Natural Science Foundation Project Fault Forecast and Maintenance Theories and Key Technologies of Complex Engineering Systems and the key project of the 11^th Five-year National Plan Industry Large-scale Equipment MRO Support System.By using Bayesian networks and D-S evidence theory,this dissertation put forward a new scheme and implementation method to solve the uncertainty problems in mechanical fault diagnosis,and made an experimental verification on the rotor system.The main research and innovation are as follows.1) On the analysis of the uncertainty source by considering its uncertainties in the process of mechanical fault diagnosis,this dissertation proposed a new consultation scheme based on uncertainty theories to solve its uncertainty problems. In the scheme,the uncertainty model was built by combining prior experience from experts with data from sensors,and the diagnosis results were obtained rapidly and accurately by probabilistic reasoning and knowledge integration.2) To solve modeling problems existing in the mechanical fault diagnosis,an uncertainty modeling method based on the fault Bayesian network was proposed.The transformation from fault tree model to fault Bayesian network model was realized by establishing the mapping relationship between them.Due to the lack of fault tree model under many circumstances,a structure learning method based on ant colony optimization algorithm was proposed.In this way,the fault Bayesian network model could be obtained from sensor da(?)a.3) A probabilistic reasoning method based on junction tree algorithm was proposed to solve NP hard problems existing in complex mechanical fault reasoning. The method consists of the transformation of Bayesian networks,the initialization of beliefs,the propagation of beliefs,and the calculation of fault probability,whose main advantage lies in the fact that the complexity of probabilistic reasoning is reduced significantly by using the local calculation.4) An information fusion model for consultation diagnosis was proposed based on D-S evidence theory.By analyzing the closeness degree between the fault case and the fault Bayesian networks,the model could be used to solve fusion paradox caused by different knowledge in the fault diagnosis.5) Taking rotor system as an example,Fault Bayesian Network model was built in this dissertation by analyzing the uncertainty problems existing in the fault diagnosis.A credible conclusion was drawn by resorting to the fault probabilistic reasoning.The numerical example indicates that the above-mentioned method is feasible.The dissertation conducted a study on a number of core theories and technologies in process of mechanical fault diagnosis and made some original research achievements in the intelligent fault diagnosis technology,which could provide a new method and tool for the mechanical fault diagnosis.