{"title":"基于频谱分析的轮轨滚动噪声机械来源的确定","authors":"Qiushi Hao, Jia Ren","doi":"10.1109/ACAIT56212.2022.10137976","DOIUrl":null,"url":null,"abstract":"Acoustic emission technology has a great advantage over existing nondestructive technologies for real-time inspection, which will significantly improve the efficiency of wheel/rail defect detection. However, wheel-rail rolling noise impedes the application of acoustic emission technology in on-line operation, especially in high-speed or heavy-load condition. The key problem lies in that current researches haven’t developed adequate knowledge of the noise, making it difficult to gain the defect signal under the strong noise. To study mechanical originations of the noise and reveal its intrinsic properties, a spectral analysis method is proposed based on a fractal description of rough surfaces. Power spectra of the surface and those of the noise, as well as the relation of their fractal dimensions, are investigated. Then, under the instruction of spectral distributions of microscopic mechanical behaviors, the noise originations and influence of the vehicle speed are determined. It is found that the noise is generated based on the surface topography, while sliding friction, particle behavior, and abrasive wear are the main mechanical sources. The sliding friction dominates among the three behaviors. The speed promotes all the behaviors and then enhances the power level, while its effects on the sliding friction is relatively severer. The work offers a theoretical basis and mechanical explanation for the noise, which provides further guidance for the real-time detection of defect signals.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of the Mechanical Origination of Wheel-Rail Rolling Noise Based on Spectrum Analysis\",\"authors\":\"Qiushi Hao, Jia Ren\",\"doi\":\"10.1109/ACAIT56212.2022.10137976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Acoustic emission technology has a great advantage over existing nondestructive technologies for real-time inspection, which will significantly improve the efficiency of wheel/rail defect detection. However, wheel-rail rolling noise impedes the application of acoustic emission technology in on-line operation, especially in high-speed or heavy-load condition. The key problem lies in that current researches haven’t developed adequate knowledge of the noise, making it difficult to gain the defect signal under the strong noise. To study mechanical originations of the noise and reveal its intrinsic properties, a spectral analysis method is proposed based on a fractal description of rough surfaces. Power spectra of the surface and those of the noise, as well as the relation of their fractal dimensions, are investigated. Then, under the instruction of spectral distributions of microscopic mechanical behaviors, the noise originations and influence of the vehicle speed are determined. It is found that the noise is generated based on the surface topography, while sliding friction, particle behavior, and abrasive wear are the main mechanical sources. The sliding friction dominates among the three behaviors. The speed promotes all the behaviors and then enhances the power level, while its effects on the sliding friction is relatively severer. The work offers a theoretical basis and mechanical explanation for the noise, which provides further guidance for the real-time detection of defect signals.\",\"PeriodicalId\":398228,\"journal\":{\"name\":\"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACAIT56212.2022.10137976\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACAIT56212.2022.10137976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Determination of the Mechanical Origination of Wheel-Rail Rolling Noise Based on Spectrum Analysis
Acoustic emission technology has a great advantage over existing nondestructive technologies for real-time inspection, which will significantly improve the efficiency of wheel/rail defect detection. However, wheel-rail rolling noise impedes the application of acoustic emission technology in on-line operation, especially in high-speed or heavy-load condition. The key problem lies in that current researches haven’t developed adequate knowledge of the noise, making it difficult to gain the defect signal under the strong noise. To study mechanical originations of the noise and reveal its intrinsic properties, a spectral analysis method is proposed based on a fractal description of rough surfaces. Power spectra of the surface and those of the noise, as well as the relation of their fractal dimensions, are investigated. Then, under the instruction of spectral distributions of microscopic mechanical behaviors, the noise originations and influence of the vehicle speed are determined. It is found that the noise is generated based on the surface topography, while sliding friction, particle behavior, and abrasive wear are the main mechanical sources. The sliding friction dominates among the three behaviors. The speed promotes all the behaviors and then enhances the power level, while its effects on the sliding friction is relatively severer. The work offers a theoretical basis and mechanical explanation for the noise, which provides further guidance for the real-time detection of defect signals.