A Novel Dynamic Weighing Method for Checkweighers Based on IWOA-SWFTD-SSI

To address the issue of reduced weighing accuracy in conveyor belt checkweighers, resulting from the severe contamination of load cell signals caused by mechanical vibrations, measurement noise, load displacement, and other interferences, this article proposes a novel dynamic weighing method based on the stationary wavelet forced threshold denoising and system identification optimized by the improved whale optimization algorithm (IWOA-SWFTD-SSI). First, the stationary wavelet transform (SWT) is applied to decompose the load cell signal. Based on the frequency characteristics of both ideal and interference signals, a forced threshold function is designed to process the detail coefficients obtained from the decomposition. The denoised signal is then reconstructed through inverse SWT using the processed detail coefficients and original approximation coefficients, effectively suppressing various interference noises. Second, based on the denoised signal, the recursive least squares algorithm is adopted to identify the parameterized model derived from prior knowledge of the checkweigher, and the model parameters are used to calculate the mass of the load. Finally, IWOA is introduced to adaptively optimize multiple parameters of the SWFTD-SSI, further enhancing the weighing accuracy. To validate the effectiveness of the proposed algorithm, extensive loading tests are conducted using loads of different masses under various operating conditions. Experimental results demonstrate that the proposed algorithm outperforms all comparison algorithms in weighing accuracy and consistently meets the accuracy requirements of class XIII(1) checkweighers as specified in OIML R 51 across a wide range of operating conditions.