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Sensors 2016, 16(6), 795; doi:10.3390/s16060795

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

1
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
2
Department of Information Engineering, Guangxi College of Water Resources and Electric Power, Nanning 530023, China
3
College of Engineering, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vamsy P. Chodavarapu
Received: 25 March 2016 / Revised: 17 May 2016 / Accepted: 23 May 2016 / Published: 31 May 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [9702 KB, uploaded 31 May 2016]   |  

Abstract

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL. View Full-Text
Keywords: tool condition monitoring (TCM); remaining useful life (RUL); wireless sensor; wavelet analysis; wavelet packet transform (WPT); neuro-fuzzy network (NFN) tool condition monitoring (TCM); remaining useful life (RUL); wireless sensor; wavelet analysis; wavelet packet transform (WPT); neuro-fuzzy network (NFN)
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).

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MDPI and ACS Style

Zhang, C.; Yao, X.; Zhang, J.; Jin, H. Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations. Sensors 2016, 16, 795.

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