Data Mining Application In Fault Diagnose Of Bearing

This paper is backgrounded on the project of The Second Technology Alteration and Add-content for the Test of Bearing's Fatigue and Life, which is cooperated by Dalian Maritime University Automation Institute and the Bearing Test Inspiring Center of WangFangDian Bearing Group. Combined with the current development status of the research on bearing's life at home and abroad, aiming at the fact that data-base and data warehouse technology have been applicated in fault diagnose field widely and the reality of the installation of on-line and off-line monitoring system to the significant equipments and large-scale databases and data warehouses forming in the field of fault diagnosis, a new method is presented that is using Data Mining technology in the field of bearing's fault diagnosis to find out the latent knowledge in the typical fault data and finish the identification of representative faults. At the same time, we make an attempt at the forecast on the bearing's life.The two Data Mining methods of Decision Tree and Association Rule can produce visible rules and solve the discovery of knowledge in the magnanimity data effectively. The Decision Tree has a tree construction like flow chart, whose main usage is to pick up classified rules and predict respectively. The Association Rule is to find out interesting rules among the data items and generate rules.In this paper C++ Builder's own database is chosen to be the database management system for storing the status signals of the equipments and build simple database for saving the data status of the equipments. Adopting the object oriented program design and modularization program design method by using C++ Builder empolder tool in Windows 98, the software of Bearing's Fault Diagnosis system based on Data Mining is realized.Due to Data Mining application in bearing's fault diagnosis is a new method and it's not high-point by only using one of them, the two Data Mining method of Decision Tree and Association Rule are chosen to resolve factual problem. Facts have proved that using multi-methods and combined with each other can get valuable rules of bearing's fault according to material problem.In addition, SPSS is applied in the last part of this paper. Curve estimation in the Regression Analysis Principle is used to simulate and compose peak curve to predict the bearing's fatigue and life. The final experimental result shows that we can forecast the bearing's life more accurately as long as an exact prediction model is built.