Data Mining Technology And Application In Fault Diagnosis Of Locomotive

Data Mining, a new generation of tools and techniques for automatic and intelligent database analysis, is an active area with the promise for a high payoff in many business and scientific application. On the other hand, knowledge acquisition has been a bottleneck with the rapid development of railway information technology. To deal with this challenge, Data Mining technology is studied and applied to locomotive fault diagnose in this paper.The paper makes a summary of the current technology of Data Mining and analyses the feature of Rough Set in the application of Data Mining. Next, the SS8 Converter is listed as study target, through the analysis of the Converter's work principle, the Converter's simulated model is founded. According to Converter's output voltage for analyzed target of fault information, the paper simulates the operation of the Converter different components in disconnection fault and structures relevant output waveforms. It adopts wavelet transform to make the power decomposition to waveforms and constructs relevant character vectors.Owing to the redundant character vector, it might cause some faults which cannot be recognized. Therefore, the paper adopts the technology based on Data Mining of Rough Set for Converter fault diagnose, study the method of fault character reduction and diagnose rule obtainer. The effective rule sets are acquired in terms of reduction of existing rules under remain identify ability conditions. When the results apply to the simulate data, it indicates all above methods have stronger robustness and general ability.Data Mining research are on the stage of developing. A lot of problems is worthy of discussing about Data Mining itself and its application in locomotive fault. The study of this paper is just to have a try, relevant work needs further study.