Online tool condition monitoring based on parsimonious ensemble

Abstract

Existing methodologies for tool condition monitoring (TCM) still rely on batch approaches which cannot cope with a fast sampling rate of a metal cutting process. Furthermore, they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online TCM approach based on Parsimonious Ensemble+ (pENsemble+). The unique feature of pENsemble+ lies in its highly flexible principle where both the ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. This paper presents advancement of a newly developed ensemble learning algorithm, pENsemble, where the online active learning scenario is incorporated to reduce the operator's labeling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilizing two real-world manufacturing data streams: 1) metal turning and 2) 3-D-printing processes and comparisons with well-known algorithms were carried out. Furthermore, the efficacy of pENsemble+ was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of the operator's labeling effort.Ministry of Education (MOE)Nanyang Technological UniversityThe work of M. Pratama was supported in part by NTU start-up Grant and in part by MOE Tier 1 Grant RG130/17. The work of W. Pedrycz was supported in part by the Natural Sciences and Engineering Research Council of Canada and in part by Canada Research Chair in Computational Intelligence. The work of E. Lughofer was supported by the Austrian COMET-K2 Programme of the Linz Center of Mechatronics, funded by the Austrian Federal Government and the Federal State of Upper Austria. This paper was recommended by Associate Editor J. Q. Gan.