Research And Development Of Fault Diagnosis Expert System For Elevators

Chinese economy is developing rapidly in recent decades, and the average height of the buildings is also increasing. The elevator has become indispensable important tool for people to live and work. The reliability and safety of the elevator threats people’s life and property at every moment, so it’s necessary to ensure the elevator fault detection and treatment on time. To ensure the reliability of the elevator two things need to be done:one is to test the reliability of the elevator before the elevator officially leaves the factory to be installed; the other is timely detection and treatment when the operation of the elevator fails.According to the current needs of the elevator fault diagnosis, this thesis studies the theory of expert system and support vector machine. The support vector machine is integrated into the expert system to achieve precise reasoning, using the support vector machine’s data mining capability to analyze the elevator fault reasons from the fault data. The expert system establishes, manages and maintains the knowledge base, and uses rules to reasoning the elevator faults and makes appropriate explanations by the interpreter. and provides the explanation mechanism. The system gives full play to their advantages to improve the diagnostic ability. The main contents of this thesis are as follows:Firstly, this thesis introduces the technology of fault diagnosis, analyzes and compares the fault diagnosis methods, and introduces several typical fault diagnosis expert system, and analyzes the common faults of the elevator. Secondly, this thesis builds an elevator fault diagnosis expert system model, selects the production rule representation, forward reasoning to represents and reasonings knowledge respectively. Thirdly, this thesis researches elevator fault diagnosis algorithm, including the basic principle of support vector machine and several key points; builds the support vector machine fault diagnosis model with Radial Basis Function uses Cross-validation and Grid-search to select the parameters, and using "one-versus-one" to deal with fault diagnosis. Finally, this thesis adopts the method of combining expert system and support vector machine to build fault diagnosis expert system for elevators, which implements the expert system knowledge acquisition, knowledge representation, knowledge base, reasoning machine and the interpreter.