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摘要
Rolling poultry eggs through a weighing rail is a simple, fast, and efficient way to measure mass. However, disturbances generated primarily by the rolling eggs vary with the rolling state and speed as well as with the egg mass and shape. These disturbances distort the load cell signal in the form of a stochastic strong amplitude fluctuation, and eliminating these fluctuations completely is a difficult task. A piezoelectric accelerometer was used to measure the vibration disturbances. The smoothed pseudo-Wigner-Ville distribution (SPWVD), which is a type of time-frequency analysis, was used to analyze the vibration disturbances using the accelerometer data. The results indicated that the disturbances were non-stationary, and the frequency characteristics were time-varying. Preliminary experiments showed that the commonly used low-pass filtering and subsequent average-based mass estimation method (AME) did not result in a satisfactory weighing accuracy. To meet the requirement of fast and accurate dynamic weighing of eggs, this research proposed a sorting-based mass estimator (SME) that consisted of an optimized digital filter and asymmetrically trimmed mean. The SME regarded the mass measurement as a problem of location estimation of non-Gaussian and heavy-tailed random variables, given the short observation time and the presence of outliers (disturbances). Four types of digital filters in the SME were selected to pre-filter the load cell data. The relevant parameters of the digital filters and asymmetrically trimmed mean in the SME were optimized using a grid search. Experimental results showed that the proposed SME effectively improved the weighing accuracy, and almost all of the egg weighing errors were less than 1 g with a processing speed of up to 5 eggs s(-1). Compared with the AME, the overall mean error was reduced by approximately 86% to 93%, and the overall standard deviation of the error (SDE) was reduced by approximately 41% to 50%.
- 出版日期2017
- 单位浙江大学
