Brake Discs Surface Defect Detection Using the IGD-IHT Algorithm and the PIQEDS-ISSA-NESN Algorithm

作     者：Feng Li Zhen Yu Juan Gao Qi An 

作者机构：Haier School(Electromechanical School)Qingdao Technical College Institute of Oceanographic InstrumentationQilu University of Technology (Shandong Academy of Sciences) State Key Laboratory of Precision Measuring Technology and InstrumentTianjin University School of InformaticsQingdao Technical College Department of Mechanical EngineeringTsinghua University 

出 版 物：《Instrumentation》 (仪器仪表学报(英文版))

年 卷 期：2024年

页      面：62-73页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 082304[工学-载运工具运用工程] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：bankrolled by the West Coast New Area University President Fund Special Project of Qingdao Technical College (Grant No.39100101) the National Key Research and Development Plan (Grant No.2017YFF0108100) the Basic Research Projects of Science,Education,Industry Integration Pilot Project of the Qilu University of Technology (Shandong Academy of Sciences)(Grant No.2023PX031) the Natural Science Foundation of Qingdao under Grants No.23-2-1-12 1-zyyd-jch the project ZR2023QE212 supported by Shandong Provincial Natural Science Foundation 

摘      要：As one of the core parts,the brake discs directly impact the braking and safety performance of *** surface detection methods of the brake disc have poor robustness due to their reliance on manual feature extraction.A detection instrument was designed to focus on the *** features were extracted using the improved Gaussian difference algorithm and Hough transform algorithm(IGD-IHT).An identification method for brake disc surface defects was designed in this paper based on the Perception-based Image Quality Evaluator and Dempster rule-improved sparrow search algorithm-Nonlinear echo state network(PIQEDS-ISSA-NESN) to better *** was shown in the experiment that the accuracy was more than 97%,the false alarm rate was less than 1.5%,and the false alarm rate was less than 1.5%.