Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

作     者：Kundur Shantisagar R.Jegadeeshwaran G.Sakthivel T.M.Alamelu Manghai 

作者机构：School of Mechanical and Building SciencesVellore Institute of TechnologyChennaiTamil NaduIndia 

出 版 物：《Structural Durability & Health Monitoring》 (结构耐久性与健康监测（英文）)

年 卷 期：2019年第13卷第3期

页      面：303-316页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：VIT University 

主　　题：Statistical features J48 decision tree algorithm confusion matrix fuzzy logic weka 

摘      要：The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting *** research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration *** vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition *** descriptive statistical features were extracted from the acquired vibration signal using the feature extraction *** extracted statistical features were selected using a feature selection process through J48 decision tree *** selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive *** decision tree model produced the classification accuracy as 94.78%with five selected *** developed fuzzy model produced the classification accuracy as 94.02%with five membership ***,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.