Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

Abstract

In various machining operations, the tool condition monitoring (TCM) is highly necessary to avoid uncertain downtime in production. TCM provides continuously the condition of cutting tool by noticing various parameters such as temperature, acoustic emission and vibration. One of the best ways to monitor the condition of cutting tools for unmanned machining is by observing tool vibration signature. In the present work, vibration signals are acquired from the cutting tool. One healthy state and three faulty conditions of tools are considered for the study. The faulty tools considered in the current study are worn flank, broken tool and extended overhang. The vibration signals of these faulty tool conditions are used to train the machine learning algorithm. Statistical features are extracted from the vibration signal to feed as input to the J48 decision tree. The classifier algorithm used in the current study is rotation forest algorithm. The algorithm uses only significant features which are selected from a decision tree. The algorithm is validated with test dataset to recognize the faulty or healthy state of the tool. It was found that the algorithm could classify the tool condition with 95.00% classification accuracy.

Tool Condition Monitoring for High-Performance Machining Systems—A Review

“…They evaluate the usefulness of features subsets based on their intrinsic properties using evaluation measures, such as dependency, consistency, or information, to eliminate low-ranking features [ 171 , 174 , 175 ]. The ranking measure is determined using statistical measures, such as Pearson’s correlation coefficient, the coefficient of determination, minimum redundancy maximum relevance (mRMR), or analysis of variance ANOVA [ 171 , 174 , 176 , 177 , 178 ]. A detailed discussion on various performance measures is available in [ 179 ].…”