Continuous weighing on a multi-stage conveyor belt with FIR filter

Tasaki, Ryosuke; Yamazaki, Takanori; Ohnishi, Hideo; Kobayashi, Masaaki; Kurosu, Shigeru

doi:10.1016/j.measurement.2006.05.010

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…The model-based deconvolution design of compensators implies that the measurand true value should be known a-priori for certain applications, such as mass determinations [7,9]. In the literature most of the measurements systems are assumed linear time-invariant, but the compensation digital filters can be linear [10], nonlinear [6] or time-varying [11].…”

Section: Introductionmentioning

confidence: 99%

“…An example of this activation is the measurement of mass while the to-be-weighted object is transported by a conveyor belt, and the profile of the input is a saturated ramp. Current solutions to the weighing in motion are low pass filters that estimate the mass using the saturated ramp [10,11]. The signal processing affine input estimation methods are motivated by the need to obtain the mass of the object from the ramp before it reaches saturation.…”

Section: Introductionmentioning

confidence: 99%

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Input parameters estimation from time-varying measurements

Quintana-Carapia

Markovsky

2020

Measurement

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A measurement is a dynamical process that aims to estimate the true value of a measurand. The measurand is the input that excites a sensor, and, as a consequence, the sensor output is a transient response. The main approach to estimate the input is applying the sensor transient response to another dynamical system. This dynamical system is designed by deconvolution to invert the sensor dynamics and compensate the sensor response. Digital signal processors enable an alternative approach to estimate the unknown input. There exists a data-driven subspace-based signal processing method that estimates a measurand, assuming it is constant during the measurement. To estimate the parameters of a measurand that varies at a constant rate, we extended the data-driven input estimation method to make it adaptive to the affine input.In this paper, we describe the proposed subspace signal processing method for the measurement of an affine measurand and compare its performance to a maximum-likelihood input estimation method and to an existing time-varying compensation filter. The subspace method is recursive and allows real-time implementations since it directly estimates the input without identifying a sensor model. The maximum-likelihood method is model-based and requires very high computational effort.In this form, the maximum-likelihood method cannot be implemented in real-time, however, we used it as a reference to evaluate the subspace method and the time-varying compensation filter results. The effectiveness of the subspace method is validated in a simulation study with a time-varying sensor. The results show that the subspace method estimation has relative errors that are one order of magnitude smaller and converges two times faster than the compensation filter.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Input parameters estimation from time-varying measurements

Quintana-Carapia

Markovsky

2020

Measurement

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“…It is necessary to use advanced noise reduction techniques in order to reduce the response of load cells and such noises originating from environmental effects. 6,7 In dynamic weighing systems, the intrinsic oscillations of the load cell combined with the low-frequency disturbance occurring due to the vibrations in the system make the measurement signal quite difficult to process. 4 It is considerably difficult to separate this disruptive effect originating from the vibrations in the system from the measurement signal.…”

Section: Introductionmentioning

confidence: 99%

Digital Signal Processing–based Dynamic Mass Measurement System for Egg Weighing Process

Yabanova

2017

Measurement and Control

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In this study, an electronic, mechanical and software system, where the weight measurements of eggs can be performed dynamically, is developed. As the speed is an important factor in the production sector, it is of significant importance that the manufactured products be weighed in a rapid and correct manner. For this reason, systems where the products are dynamically weighed are developed. However, as the products are weighed while they are moving in dynamic weighing systems, undesired disturbing effects occur on the measurement signal. The product weights must be measured at required speeds by eliminating this disturbing effect. Dynamic weighing is performed using a load cell. A digital signal processing–based card has been developed to measure the signal received from the load cell and to send it to the computer. The eggs are weighed while they are moving as they roll over the load cell. A program has been developed using the LabVIEW program to receive, filter and analyze the data read and sent to the computer by digital signal processing. In addition, the configuration adjustments of the integrated analog-to-digital converter that reads data from the load cell can also be performed thanks to this program.

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“…Further efforts to enhance continuous measurement accuracy for high speed mass measurement have been performed [30,31]. In respect of analysis method, the algorithm adapting frequency analysis [32,33] or digital filter [34] was attempted. In this paper, the noise in load cell signal in an automatic weighing system based on a belt conveyor is analyzed, and the method to reduce the noise component in load cell sensor is investigated.…”

Section: Introductionmentioning

confidence: 99%

Noise in Load Cell Signal in an Automatic Weighing System Based on a Belt Conveyor

Choi

2017

Journal of Sensors

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Noise in load cell signal in an automatic weighing system based on a belt conveyor has been examined experimentally in time and frequency domains to enhance signal quality. The noise frequency spectrum showed nonlinearly increasing multiple resonance peaks as speed increased. The noise reduction process using noise reduction algorithm, by sharply rejecting peak noise frequency component and afterward forming optimum pulse width ratio through filter slope control using selective switching of 6 LPF stages, was used for enhanced accuracy. The effectiveness of proposed method, controlling both cutoff frequency and slope of LPF, was evaluated by feeding 50 g test mass, and this noise reduction process showed better noise filtering with enhanced accuracy than fixed cutoff frequency control method. The ratio of top to bottom pulse width showed that LPF cutoff frequency above 5 Hz had the ratio above 50% up to 80 m/min speed range.

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Continuous weighing on a multi-stage conveyor belt with FIR filter

Cited by 12 publications

References 2 publications

Input parameters estimation from time-varying measurements

Input parameters estimation from time-varying measurements

Digital Signal Processing–based Dynamic Mass Measurement System for Egg Weighing Process

Noise in Load Cell Signal in an Automatic Weighing System Based on a Belt Conveyor

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