Application Research Of Bayesian Networks Into Coudition-Based Maintenance System Of Hydroelectric Set

In the past, maintenance method of hydroelectric set often takes plan-maintenance mode, which is executed according to a fix period. But because there is individual difference in all kinds of parts in hydroelectric set, and environment and affect factor are different, the hydroelectric set maintenance method executed as a fixed period will bring insufficient maintenance and superfluous maintenance. So traditional maintenance mode have many shortcomings in fact. Condition-based maintenance is a kind of predictive maintenance mode, also be called Predictive-Maintenance. It can estimate the deterioration status of hydroelectric set by analyzing the data and the information provided by status monitor system and fault diagnosis system, and plan accurate maintenance schedule, it can promote reliability of hydroelectric set and reduce maintenance cost.Hydroelectric set is a complex nonlinear dynamical system, there are many uncertainty in appearance of fault, traditional modeling theory and method of fault diagnosis have difficulties in describing uncertainty accurately, which leads condition-based maintenance system of hydroelectric set have difficulties to get more precise diagnosis conclusion and to be applied into practice. So new theory and methods must be introduced to build more effective model and promote practicability of condition-based maintenance system of hydroelectric.Bayesian Networks is a kind of important modeling, inference and machine learning tool in complex system. It integrates probability theory and graph theory, it can perfectly quantizing uncertainty generally in complex system and can provide more precise result based on its probability inference, in the same time the system model based on Bayesian Networks has more intelligence. With more deeply research of Bayesian Networks of theory and implementation methods, specially machine learning methods of Bayesian Networks become more and more perfect, Bayesian Networks are applied into more and more fields and show its good future.The dissertation applies the theory and methods of Bayesian Networks into condition-based maintenance system of hydroelectric set, and study how to construct the fault diagnosis system and maintenance decision system, the general decision strategy of maintenance and test is put forward. In the same time as application research, the basic theory and methods of Bayesian Networks are introduced and discussed, and PPTC probability inference algorithm is improved. In the last, in order to solve the difficulties in really practice, ME learning algorithm under incomplete data set is brought forward, which leads the whole system has good self-learning ability and paves the road of applying Bayesian Networks into practice. The major content in this dissertation can be separated into some parts listed below:(1) Studying the general implementation methods and processes of hydroelectric set condition-based maintenance system. Putting forward the architecture of condition-based maintenance system. Discussing the signal collection, character extraction and status recognition in condition-based maintenance system.(2) Studying the vibration fault mechanism and the fault symptom in detail, summarizing traditional diagnosis methods of vibration fault, according to the requirement of fault diagnosis, presenting the laying methods of survey points in condition-based maintenance system.(3) Based on PPTC inference algorithm of Bayesian Networks, presenting a kind of optimized PPTC algorithm, and designing a kind of data structure to improve the performing efficiency of PPTC algorithm.(4) Applying the theory and methods of Bayesian Networks, with several typical vibration fault as study objects, Building a simple fault diagnosis expert system of hydroelectric set-SmartHydro. Through the modeling, inference and result analysis process of expert system, studying the application theory and method.(5) Based on theory of System Utility, presenting the general strategy of hydroelectric set maintenance and test decision. By extend...