Abstract:Aiming to address typical operational failures such as belt breakage and pickup tooth detachment in film?recovery machines, a fault monitoring system for the pickup mechanism was designed. This system employed an STM32 microcontroller as the data acquisition core and utilized the Qt framework to develop the upper?level monitoring interface. To enhance field interference resistance, RS485 bus communication was adopted for data transmission. Considering the circumferential arrangement of the pick?up teeth, the film?stripping roller speed and the time interval between adjacent teeth passing sensors were selected as key monitoring parameters. Through screening sample data under normal operating conditions and comparing polynomial, exponential, and other function fits, an optimal second?order exponential decay function model was established for the relationship between rotational speed and theoretical time interval values under normal conditions. The natural exponential function was introduced to calculate the relative deviation between monitored and theoretical values, enhancing data feature differentiation. Normalized rotational speed, time interval values, and their relative deviations were used as feature vectors input into a hyperparameter?optimized multilayer perceptron (MLP) for classification. The Softmax function outputted fault states, achieving an overall model training recognition accuracy of 98.65%. Model comparison and bench validation tests demonstrated that the MLP outperformed support vector machines and random forest models. Bench validation achieved an average system recognition accuracy of 96.13%. These findings can provide theoretical and technical references for developing fault monitoring systems for gear?belt residual film recovery machines.
