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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.


Typical slow-changing quadratic acceleration profile.
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f6-sensors-09-03767: Typical slow-changing quadratic acceleration profile.

Mentions: As it was demonstrated in Section 2.1, the finite-difference effects for estimating the derivative of a signal are more severe when the signal has a slow-changing rate; therefore, a slow-changing quadratic acceleration profile is utilized for testing the proposed methodology, as shown in Figure 6. This profile was generated with a positive-only quadratic waveform at 12-bit resolution and spread along 4,096 samples. Finite differences of this profile has an expected absolute quantized maximum of 2, when these differences are calculated directly, giving an effective resolution of around 2 bits for the estimation, which is highly corrupted with quantization noise. The methodology is applied to demonstrate its efficiency by improving the effective resolution of the estimation.


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)

Typical slow-changing quadratic acceleration profile.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-09-03767: Typical slow-changing quadratic acceleration profile.
Mentions: As it was demonstrated in Section 2.1, the finite-difference effects for estimating the derivative of a signal are more severe when the signal has a slow-changing rate; therefore, a slow-changing quadratic acceleration profile is utilized for testing the proposed methodology, as shown in Figure 6. This profile was generated with a positive-only quadratic waveform at 12-bit resolution and spread along 4,096 samples. Finite differences of this profile has an expected absolute quantized maximum of 2, when these differences are calculated directly, giving an effective resolution of around 2 bits for the estimation, which is highly corrupted with quantization noise. The methodology is applied to demonstrate its efficiency by improving the effective resolution of the estimation.

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.