TY - JOUR AU1 - Yin, Yan AU2 - Zhou, Heng AU3 - Bao, Jiusheng AU4 - Li, Zengsong AU5 - Xiao, Xingming AU6 - Zhao, Shaodi AB - PurposeThis 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/approachA 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).FindingsFinally, 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/valueThen 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. TI - Fusion monitoring of friction temperature rise of mechanical brake based on multi-source information and AI technology JF - Sensor Review DO - 10.1108/SR-01-2020-0006 DA - 2020-05-11 UR - https://www.deepdyve.com/lp/emerald-publishing/fusion-monitoring-of-friction-temperature-rise-of-mechanical-brake-5qS8df9Dcw SP - 367 EP - 375 VL - 40 IS - 3 DP - DeepDyve ER -