Next Article in Journal
Wind-Induced Fatigue and Asymmetric Damage in a Timber Bridge
Next Article in Special Issue
Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks
Previous Article in Journal
Dynamic Pose Estimation Using Multiple RGB-D Cameras
Previous Article in Special Issue
Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3866; https://doi.org/10.3390/s18113866

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

1,2
and
2,*
1
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
2
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
*
Author to whom correspondence should be addressed.
Received: 29 September 2018 / Revised: 1 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
Full-Text   |   PDF [8382 KB, uploaded 10 November 2018]   |  

Abstract

Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods. View Full-Text
Keywords: tool condition monitoring; milling process; multisensor fusion; kernel extreme learning machine; genetic algorithm tool condition monitoring; milling process; multisensor fusion; kernel extreme learning machine; genetic algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhou, Y.; Xue, W. A Multisensor Fusion Method for Tool Condition Monitoring in Milling. Sensors 2018, 18, 3866.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top