文献类型：期刊文献

英文题名：Random Load Pattern Recognition of Test Road Based on a Laser Direct Writing Carbon-Based Strain Sensor and a Deep Neural Network

作者：Ju, Kuan[1];Weng, Shuo[2];Li, Chun[1];Zhao, Lihui;Li, Bo[1];Hu, Yue[1];Gao, Yang[1,3];Xuan, Fuzhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect, Shanghai 200237, Peoples R China;[2]Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai 200093, Peoples R China;[3]Wuhan Text Univ, State Key Lab New Text Mat & Adv Proc Technol, Wuhan 430200, Peoples R China

年份：2023

卷号：72

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20234014841353);WOS:【SCI-EXPANDED(收录号:WOS:001082374600015)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 52275146, Grant 61804054, and Grant 12174102;and in part by the State Key Laboratory of New Textile Materials and Advanced Processing Technologies under Grant FZ2022006. The Associate Editor coordinating the review process was Dr. Bruno Ando.

语种：英文

外文关键词：Deep neural network (DNN); edge computing; load condition recognition; signal processing; strain sensor

摘要：The practical application of hydrogen-based fuel cell vehicles (HFCVs) requires the design of advanced stress monitoring system on the hydrogen storage devices, relying on accurate feedback and recognition from the vehicles load conditions. Herein, we proposed an intelligent monitoring method for continuous monitoring of dynamic load parameters and load recognition by combining carbon-based strain sensor, data acquisition and wireless transmission embedded unit, and machine learning (ML). The carbon-based strain sensor, prepared by laser direct writing (LDW) on polyimide (PI) film, exhibited a sensitivity of 6.08 and a frequency response of 1 Hz for effectively measuring the strain status under varying road conditions. A miniaturized and highly integrated data acquisition and wireless transmission unit was adopted to implement edge computing and wireless communication. Wavelet packet decomposition (WPD) and Hilbert-Huang transform (HHT) were fused to extract intrinsic features in random load spectrum, and the distance-based feature evaluation method was used to evaluate sensitive features to optimize the structure of the deep neural network (DNN). The DNN model could effectively recognize six types of test road conditions, with a test accuracy over 90%. This research effectively advances the design of the loading condition monitoring system of the HFCVs hydrogen storage device.