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Robust period estimation of automated cutting systems by improved autocorrelation & linear regression techniques

Anthony McAtear (UGent) , Ruben Gielen and Nilesh Madhu (UGent)
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Abstract
Condition monitoring is an important asset in the industry to improve the safety and efficiency of the production chain. However, in heavy machinery – such as edge trimmers in steel mills – it is often impractical and unsafe to install intrusive sensors to get the data needed for condition monitoring. Non-intrusive monitoring techniques based, e.g., on acoustic data captured by microphones placed in the vicinity of the assembly being monitored are attractive options. Our application deals with the acoustic monitoring of rotational blades cutting steel strips at high speeds. Knowing the correct period of the cutting process is important for quality evaluation purposes. We propose two novel robust methods to estimate the periodicity based on the audio captured by a microphone near the blades. One is an improved autocorrelation function and the other is based on linear regression, both using incorporating an novel test for the correctness of the estimated period. We compare our methods against the standard autocorrelation-based periodicity measurement techniques on real data recordings. The proposed method estimates the correct period about 87% of the time, compared to an accuracy of only 51% using standard periodicity measurement approaches.
Keywords
Period estimation, linear regression, autocorrelation, condition monitoring, non-intrusive system parameter estimation

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MLA
McAtear, Anthony, et al. “Robust Period Estimation of Automated Cutting Systems by Improved Autocorrelation & Linear Regression Techniques.” 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), IEEE, 2021, pp. 1901–05, doi:10.23919/Eusipco47968.2020.9287499.
APA
McAtear, A., Gielen, R., & Madhu, N. (2021). Robust period estimation of automated cutting systems by improved autocorrelation & linear regression techniques. In 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) (pp. 1901–1905). Amsterdam, Netherlands (online): IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287499
Chicago author-date
McAtear, Anthony, Ruben Gielen, and Nilesh Madhu. 2021. “Robust Period Estimation of Automated Cutting Systems by Improved Autocorrelation & Linear Regression Techniques.” In 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 1901–5. IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287499.
Chicago author-date (all authors)
McAtear, Anthony, Ruben Gielen, and Nilesh Madhu. 2021. “Robust Period Estimation of Automated Cutting Systems by Improved Autocorrelation & Linear Regression Techniques.” In 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 1901–1905. IEEE. doi:10.23919/Eusipco47968.2020.9287499.
Vancouver
1.
McAtear A, Gielen R, Madhu N. Robust period estimation of automated cutting systems by improved autocorrelation & linear regression techniques. In: 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020). IEEE; 2021. p. 1901–5.
IEEE
[1]
A. McAtear, R. Gielen, and N. Madhu, “Robust period estimation of automated cutting systems by improved autocorrelation & linear regression techniques,” in 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), Amsterdam, Netherlands (online), 2021, pp. 1901–1905.
@inproceedings{8678460,
  abstract     = {{Condition monitoring is an important asset in the industry to improve the safety and efficiency of the production chain. However, in heavy machinery – such as edge trimmers in steel mills – it is often impractical and unsafe to install intrusive sensors to get the data needed for condition monitoring. Non-intrusive monitoring techniques based, e.g., on acoustic data captured by microphones placed in the vicinity of the assembly being monitored are attractive options. Our application deals with the acoustic monitoring of rotational blades cutting steel strips at high speeds. Knowing the correct period of the cutting process is important for quality evaluation purposes. We propose two novel robust methods to estimate the periodicity based on the audio captured by a microphone near the blades. One is an improved autocorrelation function and the other is based on linear regression, both using incorporating an novel test for the correctness of the estimated period. We compare our methods against the standard autocorrelation-based periodicity measurement techniques on real data recordings. The proposed method estimates the correct period about 87% of the time, compared to an accuracy of only 51% using standard periodicity measurement approaches.}},
  author       = {{McAtear, Anthony and Gielen, Ruben and Madhu, Nilesh}},
  booktitle    = {{28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)}},
  isbn         = {{9789082797053}},
  issn         = {{2076-1465}},
  keywords     = {{Period estimation,linear regression,autocorrelation,condition monitoring,non-intrusive system parameter estimation}},
  language     = {{eng}},
  location     = {{Amsterdam, Netherlands (online)}},
  pages        = {{1901--1905}},
  publisher    = {{IEEE}},
  title        = {{Robust period estimation of automated cutting systems by improved autocorrelation & linear regression techniques}},
  url          = {{http://dx.doi.org/10.23919/Eusipco47968.2020.9287499}},
  year         = {{2021}},
}

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