Fusion monitoring of friction temperature rise of mechanical brake based on multi-source information and AI technology

Purpose This paper aims to overcome the defect of single-source temperature measurement method and improve the measurement accuracy of FTR. The friction temperature rise (FTR) of brake affects braking performance seriously. However, it was mainly detected by single-source indirect thermometry, which has obvious deviations. Design/methodology/approach A three-point temperature measurement system was built based on three kinds of single-resource thermometry. Temperature characteristics of these thermometry were analyzed to achieve a standard FTR curve. Two fusion-monitoring models for FTR based on multi-source information were established by artificial neural network (ANN) and support vector machine (SVM). Findings Finally, the two models were verified based on the experimental results. The results showed that the fusion-monitoring model of SVM was more accurate than that of ANN in monitoring of FTR. Originality/value Then the temperature characteristics of the three single-source thermometry were analyzed, and the fusion-monitoring models based on multi-source information were established by ANN and SVM. Finally, the accuracy of the two models was compared by the experimental results. The more suitable fusion-monitoring model for FTR monitoring was determined which would be of theoretical and practical significance for remedying the monitoring defect of FTR.