The Application Of Decision Tree Algorithm On Fault Diagnosis System For Mechanical Equipments

With the development of modern mechanical equipments in large-scale and intelligent, the structure of the mechanical equipments is more and more complicated, the fault diagnosis of equipment is becoming more and more difficult. Driven by machine learning and data mining, the fault diagnosis system based on machine self-learning has been developing rapidly. The intelligent fault diagnosis system overcomes the defects of the original diagnosis, and can find the hidden faults of mechanical equipment timely and accurately, thus monitoring and diagnosing the equipment’s fault automatically and fast, improving the diagnostic efficiency, and reducing the loss caused by the inaccurate diagnosis.In the intelligent fault diagnosis system, how to get the diagnosis rules is a key. This paper uses the decision tree technology as the main technique extracting diagnosis rules which are widely used in data mining. By analyzing and researching the existing decision tree method, I found that the decision tree Constructor method based on rough sets and variable precision rough set theory has a better effect on classifying. However there are also disadvantages, for example, the classification accuracy is not high, it’s difficult to select the decision tree node attribute, the capability of rejecting the noisy data is poor etc. Thus a improved decision trees algorithm based on variable precision rough set is proposed. Through testing it on a machine learning platform called Weka, the results show that the proposed algorithm are improved in classification accuracy, complexity and noise resistant ability. So we prove the effectiveness of the proposed algorithm.On the basis of the improved method, we designed and implemented a set of fault diagnosis and analysis system, which is targeted at the mechanical equipment in coal preparation plant. According to the history data of vibration which can reflect the running state of the equipments, we can get the diagnosis rules by using the proposed algorithm. Then the rules apply to the real-time diagnosis system, so that we can analyze and diagnose the real-time conditions of the equipments.