Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Engineering Asset Management - Systems, Professional Practices and Certification

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

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

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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):739–768

    Article  Google Scholar 

  2. Liu G, Hao J, Kuang J (2000) The machine plant accident alarm with trend and overage synthesis method. J Agric Univ Hebei 23(4):89–92

    Google Scholar 

  3. Frank PM (1995) Residual evaluation for fault diagnosis based on adaptive fuzzy thresholds. IEE Colloquium (Digest). IEE Press, London pp 1–11

    Google Scholar 

  4. Zhang Q, Xu G (2006) Incipient fault diagnosis based on moving probabilistic neural network. J Xi’an Jiaotong Univ 40(9):1036–1040

    MATH  Google Scholar 

  5. 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

    Google Scholar 

  6. 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

    Google Scholar 

  7. 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):537–553

    Article  Google Scholar 

  8. 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):53–58

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xi’an Jiaotong University, 710049, Xi’an, China

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

Authors
  1. Xiaoguang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guanghua Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sicong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaoguang Chen , Guanghua Xu , Fei Liu , Xiang Wan , Qing Zhang or Sicong Zhang .

Editor information

Editors and Affiliations

  1. Systems Engineering, City University of Hong Kong, Hong Kong, China

    Peter W. Tse

  2. Asset Institute, Brisbane, Australia

    Joseph Mathew

  3. Community, Construction Professionals' Development Centre, Hong Kong, China

    King Wong

  4. Engineering Doctorate Society, Hong Kong, China

    Rocky Lam

  5. Kam Yuen (Group) International Limited, Hong Kong, China

    C.N. Ko

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chen, X., Xu, G., Liu, F., Wan, X., Zhang, Q., Zhang, S. (2015). An Adaptive Alarm Method for Tool Condition Monitoring Based on Probability Density Functions Estimated with the Parzen Window. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_1

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09506-6

  • Online ISBN: 978-3-319-09507-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation