{"title":"利用稀释 CNN 和解卷积估算工业物联网的振动源","authors":"","doi":"10.1016/j.iot.2024.101303","DOIUrl":null,"url":null,"abstract":"<div><p>To minimize data traffic in industrial IoT applications, vibration-based condition monitoring should be conducted on sensors and without requiring machine-specific information. The proposed method enables blind estimation of vibration sources, eliminating the need for information about the monitored equipment or external measurements. Vibrations in rotating machinery primarily originate from two sources: dominant gear-related vibrations and low-energy signals associated with bearing faults. Both sources are distorted by the machine's transfer function before reaching the sensor. This method estimates both sources in two stages: first, the gear signal is isolated using a dilated CNN; second, the bearing fault signal is estimated using the squared log envelope of the residual. The effect of the transfer function is removed from both sources using a novel whitening-based deconvolution method (WBD). Both simulation and experimental results demonstrate the method's ability to detect bearing failures early without additional information. This study considers both local and distributed bearing faults, assuming the vibrations are recorded under stable operating conditions.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating vibration sources for industrial IoT using dilated CNN and deconvolution\",\"authors\":\"\",\"doi\":\"10.1016/j.iot.2024.101303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To minimize data traffic in industrial IoT applications, vibration-based condition monitoring should be conducted on sensors and without requiring machine-specific information. The proposed method enables blind estimation of vibration sources, eliminating the need for information about the monitored equipment or external measurements. Vibrations in rotating machinery primarily originate from two sources: dominant gear-related vibrations and low-energy signals associated with bearing faults. Both sources are distorted by the machine's transfer function before reaching the sensor. This method estimates both sources in two stages: first, the gear signal is isolated using a dilated CNN; second, the bearing fault signal is estimated using the squared log envelope of the residual. The effect of the transfer function is removed from both sources using a novel whitening-based deconvolution method (WBD). Both simulation and experimental results demonstrate the method's ability to detect bearing failures early without additional information. This study considers both local and distributed bearing faults, assuming the vibrations are recorded under stable operating conditions.</p></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660524002440\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660524002440","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Estimating vibration sources for industrial IoT using dilated CNN and deconvolution
To minimize data traffic in industrial IoT applications, vibration-based condition monitoring should be conducted on sensors and without requiring machine-specific information. The proposed method enables blind estimation of vibration sources, eliminating the need for information about the monitored equipment or external measurements. Vibrations in rotating machinery primarily originate from two sources: dominant gear-related vibrations and low-energy signals associated with bearing faults. Both sources are distorted by the machine's transfer function before reaching the sensor. This method estimates both sources in two stages: first, the gear signal is isolated using a dilated CNN; second, the bearing fault signal is estimated using the squared log envelope of the residual. The effect of the transfer function is removed from both sources using a novel whitening-based deconvolution method (WBD). Both simulation and experimental results demonstrate the method's ability to detect bearing failures early without additional information. This study considers both local and distributed bearing faults, assuming the vibrations are recorded under stable operating conditions.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.