Limits...
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.


Spectra of quantization error for 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
getmorefigures.php?uid=PMC3297130&req=5

f9-sensors-09-03767: Spectra of quantization error for jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.

Mentions: On the other hand, the spectra of these quantization errors are shown in Figure 9. From this figure, it can be said that the spectral contents of quantization error at the Nyquist sampling rate is higher that the spectral contents with the oversampling approach. Then again, this improvement is present up to a certain level. Table 1 summarizes the SQNR improvement, in dB, for different oversampling rates in this case of study.


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)

Spectra of quantization error for 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

f9-sensors-09-03767: Spectra of quantization error for jerk estimation. (a) Nyquist rate. (b) 4-times oversampling. (c) 8-times oversampling. (d) 16-times oversampling. (e) 32-times oversampling.
Mentions: On the other hand, the spectra of these quantization errors are shown in Figure 9. From this figure, it can be said that the spectral contents of quantization error at the Nyquist sampling rate is higher that the spectral contents with the oversampling approach. Then again, this improvement is present up to a certain level. Table 1 summarizes the SQNR improvement, in dB, for different oversampling rates in this case of study.

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.