Research Of The Fault Testing And Diagnosis Methods Based On Data Mining

This thesis taking data mining, virtual instrument and information fusion as the theoretical bases, has studied deeply on complicated system's fault diagnosis methods and its application. The researches ensure the large-scale complicated system running effectively, safely, stably and reliably, raise the equipment production efficiency and the management level, restore the fault equipment and retrieve the economic loss caused by the fault to the greatest extent, and have the very vital practical significance on ensuring sound and rapid growth of the national economy and other aspects.This paper designs and completes fault data acquisition experiment based on the virtual instrument, which provides the sufficient data for the following research. For actual situation of complicated system, this thesis mainly studies the application of attributes reduction and feature extraction based on rough set, the application of fault rules mining based on decision tree algorithm, and the application of the new fault rules mining based on clustering algorithm in the incremental data. An improved algorithm of rough sets is presented, which solves the problem of low efficiency in the attribute reduction. An improved ID3 algorithm is presented by querying and processing database directly and efficiently using the embedded SQL, which improves the efficiency and feasibility. An improved clustering algorithm is presented by combination ART2 algorithm with K-means algorithm, which restrains the ART2 cluster center drifting effectively. At the same time, this paper studies information fusion in the fault diagnosis, through the data fusion, making the data more comprehensive, through the method fusion, making diagnosises more rapid, accurate and reliable.At last, taking the three algorithms as the core and fault diagnosis of the hydraulic system as the application background, a fault testing and diagnosis system has been designed and realized. Through various functional modules'synthesis use, it realizes a series of function of data acquisition, storage, pretreatment, fault rules mining and fault matching, the report generation printing . Through actual experiment of fault diagnosis, it has proved that this system can diagnose accurately and discover the new fault rules quickly by self-learning, which satisfies the actual need of fault diagnosis.