C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter
{"title":"用于工业设施声学状态监测的敲击声探测技术","authors":"C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter","doi":"10.1109/LSENS.2024.3445162","DOIUrl":null,"url":null,"abstract":"Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638182","citationCount":"0","resultStr":"{\"title\":\"Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities\",\"authors\":\"C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter\",\"doi\":\"10.1109/LSENS.2024.3445162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.\",\"PeriodicalId\":13014,\"journal\":{\"name\":\"IEEE Sensors Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638182\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10638182/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10638182/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities
Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.