Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression

Xiao Qin, Weizhi Huang, Xuefei Wang, Zezhi Tang, Zepeng Liu

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
5 Downloads (Pure)

Abstract

Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool. To increase the prediction accuracy, it is often necessary to mount a great deal of sensors on the machine in order to collect more types of signals, which can heavily increase the cost in industrial applications. To deal with this issue, this study, for the first time, proposed a new feature network dictionary, which can enlarge the number of candidate features under limited sensor conditions, and the developed dictionary can potentially contain as much useful information as possible. This process can replace the installation of more sensors and incorporate more information. Then, the sparse augmented Lagrangian (SAL) feature selection method is proposed to reduce the number of candidate features and select the most significant features. Finally, the selected features are input to the Gaussian Process Regression (GPR) model for the RUL estimation. Extensive experiments demonstrate that our proposed RUL estimation framework output performs traditional methods, especially for the cost savings for on-line RUL estimation.
Original languageEnglish
Article number413
Number of pages21
JournalSensors
Volume23
Issue number1
DOIs
Publication statusPublished - 30 Dec 2022
Externally publishedYes

Bibliographical note

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)

Keywords

  • remaining useful life estimation
  • cutting tools
  • advanced manufacturing
  • sparse augmented lagrangian
  • gaussian process regression

Fingerprint

Dive into the research topics of 'Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression'. Together they form a unique fingerprint.

Cite this