Application of adaptive filtering to dynamic weighing of vehicles

Niedźwiecki, Maciej; A, Wasilewski

doi:10.1016/0967-0661(96)00045-7

Cited by 36 publications

(19 citation statements)

References 2 publications

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“…In order to deal with the issue of the unknown process dynamics in (Shu 1993;Niedźwiecki and Wasilewski 1996) an adaptive compensator is proposed. Adaptive methods perform simultaneously online model identification and filtering.…”

Section: State-of-the-artmentioning

confidence: 99%

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An application of system identification in metrology

Markovsky

2015

Control Engineering Practice

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Metrology is advancing by development of new measurement techniques and corresponding hardware. A given measurement technique, however, has fundamental speed and precision limitations. In order to overcome the hardware limitations, we develop signal processing methods based on the prior knowledge that the measurement process dynamics is linear time-invariant.Our approach is to model the measurement process as a step response of a dynamical system, where the input step level is the quantity of interest. The solution proposed is an algorithm that does real-time processing of the sensor's measurements. It is shown that when the measurement process dynamics is known, the input estimation problem is equivalent to state estimation. Otherwise, the input estimation problem can be solved as a system identification problem. The main underlying assumption is that the measured quantity is constant and the measurement process is a low-order linear time-invariant system. The methods are validated and compared on applications of temperature and weight measurement.

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Section: State-of-the-artmentioning

confidence: 99%

“…Adaptive methods perform simultaneously online model identification and filtering. The methods of (Shu 1993;Niedźwiecki and Wasilewski 1996) are specifically designed for weight measurement. They need nontrivial modifications for other applications.…”

Section: State-of-the-artmentioning

confidence: 99%

An application of system identification in metrology

Markovsky

2015

Control Engineering Practice

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“…Precise weight measurement is needed to generate the product labels (including the price, if the price is weight dependent) or to eliminate products that are defective (e.g., incomplete multipacks) or which do not comply with the desired weight specification (items that are outside the tolerance are automatically taken out of line) [1]. There are many other applications of dynamic weighing including weighing of cars [2], [3] and trains [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…During each weighing cycle, the model is identified based on the collected measurements. The obtained estimates of model parameters are next used to calculate the steady-state response of the weighing platform to a hypothetical step-like excitation, i.e., the static weight of the weighed item [2], [4], [9], [10]. The second, model-free filtering approach to dynamic weighing incorporates digital filters designed so as to reduce the noise and attenuate the oscillatory part of the system response.…”

Section: Introductionmentioning

confidence: 99%

High-Precision FIR-Model-Based Dynamic Weighing System

Niedźwiecki

Pietrzak

2016

IEEE Trans. Instrum. Meas.

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Conveyor belt-type checkweighers are increasingly popular components of modern production lines. They are used to assess the weight of the produced items in motion, i.e., without stopping them on the weighing platform. The main challenge one faces when designing a dynamic weighing system is providing high measurement accuracy, especially at high conveyor belt speeds. The approach proposed in this paper can be characterized as a filtering scheme based on the finite impulse response model of the weighing system response. It is shown that when such a model-based filtering is applied, the attained weighing accuracy is up to four times higher than that guaranteed by the currently available state-of-the-art solutions. Index Terms-Conveyor belt-type checkweighers, dynamic mass measurement, model-based filtering.0018-9456

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“…Niedzwiecki, et al [2] and Xu, et al [3] presented system identification methods, which use an auto-regressive (AR) model with unit step input, and a recursive least squares (RLS) algorithm to estimate the parameters. However, vehicle is a complicated system, and AR model cannot accurately characterize the WIM.…”

Section: Introductionmentioning

confidence: 99%

Weighing axle weight of moving vehicle based on empirical mode decomposition

Zhou

Cai

2008

J. Shanghai Univ.(Engl. Ed.)

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Dynamic forces are the main factor that influences the axle weight measurement accuracy of moving vehicle. Empirical mode decomposition (EMD) is presented to separate the dynamic forces contained in the axle weight signal. The concept and algorithm of EMD are introduced. The characteristic of the axle weight signal is analyzed. The method of judging pseudo intrinsic mode function (pseudo-IMF) is presented to improve the weighing accuracy. Numerical simulation and field experiments are conducted to evaluate the performance of EMD. The result shows effectiveness of the proposed method. Maximum weighing errors of the front axle, the rear axle and the gross weight at the speed of 15 km/h or lower are 2.22%, 6.26% and 4.11% respectively.

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Application of adaptive filtering to dynamic weighing of vehicles

Cited by 36 publications

References 2 publications

An application of system identification in metrology

An application of system identification in metrology

High-Precision FIR-Model-Based Dynamic Weighing System

Weighing axle weight of moving vehicle based on empirical mode decomposition

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