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Published in:
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2015 | OriginalPaper | Chapter

An Adaptive Alarm Method for Tool Condition Monitoring Based on Probability Density Functions Estimated with the Parzen Window

Authors : Xiaoguang Chen, Guanghua Xu, Fei Liu, Xiang Wan, Qing Zhang, Sicong Zhang

Published in: Engineering Asset Management - Systems, Professional Practices and Certification

Publisher: Springer International Publishing

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Abstract

Tool condition monitoring plays an important role in modern automatic processing for ensuring the processing quality and the machine life [1].

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next chapter Fault Degradation State Recognition for Planetary Gear Set Based on LVQ Neural Network
Literature
1.
Dimla DE Sr, Lister PM (2000) On-line metal cutting tool condition monitoring. I: force and vibration analyses. Int J Mach Tools Manuf 40(5):739–768CrossRef
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Zhang Q, Xu G, Hua C et al (2009) Self-adaptive alarm method for equipment condition based on one-class support vector machine. J Xi’an Jiaotong Univ 43(1):61–65
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Jemielniak K, & Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1(2):97–102
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Zhu K, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. Int J Mach Tools Manuf 49(7–8):537–553CrossRef
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Rangaraj RM, Wu Y (2010) Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows. Biomed Signal Process Control 5(1):53–58CrossRef
Metadata
Title
An Adaptive Alarm Method for Tool Condition Monitoring Based on Probability Density Functions Estimated with the Parzen Window
Authors
Xiaoguang Chen
Guanghua Xu
Fei Liu
Xiang Wan
Qing Zhang
Sicong Zhang
Copyright Year
2015
Book
Engineering Asset Management - Systems, Professional Practices and Certification

Print ISBN: 978-3-319-09506-6

Electronic ISBN: 978-3-319-09507-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-09507-3_1

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