{"title":"基于语音识别技术和最小二乘支持向量机的冲击螺栓松动检测","authors":"Furui Wang, Xuemin Chen, G. Song","doi":"10.1109/ICNSC48988.2020.9238108","DOIUrl":null,"url":null,"abstract":"In this paper, to detect bolt looseness of a subsea flange, we develop a new percussion method using speech recognition technology and least square support vector machine. Especially, to extract features from percussion-induced sound signals, we employ the mel frequency cepstral coefficient (MFCC). Finally, an experiment is conducted to verify the effectiveness of the proposed method. Compared to current detection methods for bolt loosening, the proposed method can avoid constant contact between sensors and structures, which significantly improves practicability and provides guidance for structural health monitoring based on the cyber-physics systems.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Percussion-based Detection of Bolt Looseness Using Speech Recognition Technology and Least Square Support Vector Machine\",\"authors\":\"Furui Wang, Xuemin Chen, G. Song\",\"doi\":\"10.1109/ICNSC48988.2020.9238108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, to detect bolt looseness of a subsea flange, we develop a new percussion method using speech recognition technology and least square support vector machine. Especially, to extract features from percussion-induced sound signals, we employ the mel frequency cepstral coefficient (MFCC). Finally, an experiment is conducted to verify the effectiveness of the proposed method. Compared to current detection methods for bolt loosening, the proposed method can avoid constant contact between sensors and structures, which significantly improves practicability and provides guidance for structural health monitoring based on the cyber-physics systems.\",\"PeriodicalId\":412290,\"journal\":{\"name\":\"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNSC48988.2020.9238108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNSC48988.2020.9238108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Percussion-based Detection of Bolt Looseness Using Speech Recognition Technology and Least Square Support Vector Machine
In this paper, to detect bolt looseness of a subsea flange, we develop a new percussion method using speech recognition technology and least square support vector machine. Especially, to extract features from percussion-induced sound signals, we employ the mel frequency cepstral coefficient (MFCC). Finally, an experiment is conducted to verify the effectiveness of the proposed method. Compared to current detection methods for bolt loosening, the proposed method can avoid constant contact between sensors and structures, which significantly improves practicability and provides guidance for structural health monitoring based on the cyber-physics systems.