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Novel Oversampling Technique for Improving Signal-to-Quantization Noise Ratio on Accelerometer-Based Smart Jerk Sensors in CNC Applications.

Rangel-Magdaleno JJ, Romero-Troncoso RJ, Osornio-Rios RA, Cabal-Yepez E - Sensors (Basel) (2009)

Bottom Line: The novelty of this work is the development of a smart sensor for jerk monitoring from a standard accelerometer, which has improved SQNR.The proposal is based on oversampling techniques that give a better estimation of jerk than that produced by a Nyquist-rate differentiator.Simulations and experimental results are presented to show the overall methodology performance.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro / Río Moctezuma 249, Col. San Cayetano, 76807 San Juan del Río, Querétaro, Mexico; E-Mails: jjrangel@hspdigital.org (J.J.R.-M.); raosornio@hspdigital.org (R.A.O.-R.).

ABSTRACT
Jerk monitoring, defined as the first derivative of acceleration, has become a major issue in computerized numeric controlled (CNC) machines. Several works highlight the necessity of measuring jerk in a reliable way for improving production processes. Nowadays, the computation of jerk is done by finite differences of the acceleration signal, computed at the Nyquist rate, which leads to low signal-to-quantization noise ratio (SQNR) during the estimation. The novelty of this work is the development of a smart sensor for jerk monitoring from a standard accelerometer, which has improved SQNR. The proposal is based on oversampling techniques that give a better estimation of jerk than that produced by a Nyquist-rate differentiator. Simulations and experimental results are presented to show the overall methodology performance.

No MeSH data available.


Quantization error of jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.
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f8-sensors-09-03767: Quantization error of jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.

Mentions: The quantization error, calculated by subtracting the estimated jerk from the theoretical waveform, is shown in Figure 8. The quantization noise in the jerk estimation at Nyquist sampling rate (Figure 8a) is noticeable. Quantization noise presents significant reduction with the proposed methodology from 4-times oversampling and up. Quantization noise is further reduced at higher oversampling rates, up to a certain level, where the oversampling no longer improves the SQNR. For instance, SQNR is improved from 4-times up to 16-times oversampling, but the improvement is no longer evident at 32-times oversampling.


Novel Oversampling Technique for Improving Signal-to-Quantization Noise Ratio on Accelerometer-Based Smart Jerk Sensors in CNC Applications.

Rangel-Magdaleno JJ, Romero-Troncoso RJ, Osornio-Rios RA, Cabal-Yepez E - Sensors (Basel) (2009)

Quantization error of jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3297130&req=5

f8-sensors-09-03767: Quantization error of jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.
Mentions: The quantization error, calculated by subtracting the estimated jerk from the theoretical waveform, is shown in Figure 8. The quantization noise in the jerk estimation at Nyquist sampling rate (Figure 8a) is noticeable. Quantization noise presents significant reduction with the proposed methodology from 4-times oversampling and up. Quantization noise is further reduced at higher oversampling rates, up to a certain level, where the oversampling no longer improves the SQNR. For instance, SQNR is improved from 4-times up to 16-times oversampling, but the improvement is no longer evident at 32-times oversampling.

Bottom Line: The novelty of this work is the development of a smart sensor for jerk monitoring from a standard accelerometer, which has improved SQNR.The proposal is based on oversampling techniques that give a better estimation of jerk than that produced by a Nyquist-rate differentiator.Simulations and experimental results are presented to show the overall methodology performance.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro / Río Moctezuma 249, Col. San Cayetano, 76807 San Juan del Río, Querétaro, Mexico; E-Mails: jjrangel@hspdigital.org (J.J.R.-M.); raosornio@hspdigital.org (R.A.O.-R.).

ABSTRACT
Jerk monitoring, defined as the first derivative of acceleration, has become a major issue in computerized numeric controlled (CNC) machines. Several works highlight the necessity of measuring jerk in a reliable way for improving production processes. Nowadays, the computation of jerk is done by finite differences of the acceleration signal, computed at the Nyquist rate, which leads to low signal-to-quantization noise ratio (SQNR) during the estimation. The novelty of this work is the development of a smart sensor for jerk monitoring from a standard accelerometer, which has improved SQNR. The proposal is based on oversampling techniques that give a better estimation of jerk than that produced by a Nyquist-rate differentiator. Simulations and experimental results are presented to show the overall methodology performance.

No MeSH data available.