{"title":"Telemetry Vibration Signal Analysis and Fault Detection based on Multi-scale Permutation Entropy","authors":"Hongzhou Xu, Xue Liu, Suting Qiu","doi":"10.1109/ICEICT.2019.8846312","DOIUrl":null,"url":null,"abstract":"Aiming at the characteristics of strong noise and non-stationarity of telemetry vibration signals, a multi-scale entropy-based telemetry vibration signal analysis and fault detection method was proposed. Firstly, the collected signals were modified with zero-drift and tendency eliminating. Secondly, the mutual information and Cao’s algorithm were used to select the delay time and embedding dimension, we could have more excellent performance to distinguish the abnormities of telemetry vibration signal through this step. Finally, the partial mean of multi-scale permutation entropy of all signals was calculated, which were used to distinguish the abnormities of telemetry vibration signal. The measured data demonstrated the effectiveness of this method.","PeriodicalId":382686,"journal":{"name":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEICT.2019.8846312","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Aiming at the characteristics of strong noise and non-stationarity of telemetry vibration signals, a multi-scale entropy-based telemetry vibration signal analysis and fault detection method was proposed. Firstly, the collected signals were modified with zero-drift and tendency eliminating. Secondly, the mutual information and Cao’s algorithm were used to select the delay time and embedding dimension, we could have more excellent performance to distinguish the abnormities of telemetry vibration signal through this step. Finally, the partial mean of multi-scale permutation entropy of all signals was calculated, which were used to distinguish the abnormities of telemetry vibration signal. The measured data demonstrated the effectiveness of this method.