A weight recognition method for movable objects in sealed cavity based on supervised learning

Abstract

Weight information has important reference value for the analysis of the fault source of measurement and industry. Whenthe object in the sealing cavity collides with the inner wall, it will produce undetectable weak sound signal containing weight information. Thus this paper presents a novel method to recognize the weight information of movable object in a sealed cavity. In this study, we compared and analyzed a variety of sound features, and a method combining qualitative and quantitative analysis is proposed to recognize the feature datasets that retained by feature selection. The support vector machine (SVM) algorithm was first adopted to determine the approximate range of object weight. Thereafter, the Multilayer Perceptron (MLP) based regression model was constructed to recognize the exact weight. Finally, weight detection experiment of movable particles from the sealed electronic components is utilized to verify the effectiveness of the proposed method.