Lucas Costa Brito , Gian Antonio Susto , Jorge Nei Brito , Marcus Antonio Viana Duarte
{"title":"频带相关因子 (BRF):基于旋转机械振动分析的新型自动频带选择方法。","authors":"Lucas Costa Brito , Gian Antonio Susto , Jorge Nei Brito , Marcus Antonio Viana Duarte","doi":"10.1016/j.isatra.2024.09.026","DOIUrl":null,"url":null,"abstract":"<div><div>Monitoring rotating machinery has become a fundamental activity in the industry given the high criticality in production processes. Extracting useful information from signals is a key factor for effective monitoring. Several studies in the areas of Informative Frequency Selection bands (IFB) and Feature Extraction/Selection have demonstrated the need to identify the bands of interest in vibration signals. However, typical methods in these areas focus on identifying bands where impulsive excitations are present or analyzing the relevance of features after signal extraction. Therefore, they do not focus on other regions that may be related to changes in the dynamic behavior of machines or faults. Furthermore, the methods generally require parameter adjustments and are not automatic. To overcome these problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. In other words, automatically identify all frequency bands that are related to changes in the behavior of the machines or faults. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated on a synthetically dataset and on two real datasets, showing that BRF is capable of automatically identifying bands that present relevant information for the analysis of rotating machinery.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"155 ","pages":"Pages 439-453"},"PeriodicalIF":6.3000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Band Relevance Factor (BRF): A novel automatic frequency band selection method based on vibration analysis for rotating machinery\",\"authors\":\"Lucas Costa Brito , Gian Antonio Susto , Jorge Nei Brito , Marcus Antonio Viana Duarte\",\"doi\":\"10.1016/j.isatra.2024.09.026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Monitoring rotating machinery has become a fundamental activity in the industry given the high criticality in production processes. Extracting useful information from signals is a key factor for effective monitoring. Several studies in the areas of Informative Frequency Selection bands (IFB) and Feature Extraction/Selection have demonstrated the need to identify the bands of interest in vibration signals. However, typical methods in these areas focus on identifying bands where impulsive excitations are present or analyzing the relevance of features after signal extraction. Therefore, they do not focus on other regions that may be related to changes in the dynamic behavior of machines or faults. Furthermore, the methods generally require parameter adjustments and are not automatic. To overcome these problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. In other words, automatically identify all frequency bands that are related to changes in the behavior of the machines or faults. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated on a synthetically dataset and on two real datasets, showing that BRF is capable of automatically identifying bands that present relevant information for the analysis of rotating machinery.</div></div>\",\"PeriodicalId\":14660,\"journal\":{\"name\":\"ISA transactions\",\"volume\":\"155 \",\"pages\":\"Pages 439-453\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0019057824004579\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019057824004579","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Band Relevance Factor (BRF): A novel automatic frequency band selection method based on vibration analysis for rotating machinery
Monitoring rotating machinery has become a fundamental activity in the industry given the high criticality in production processes. Extracting useful information from signals is a key factor for effective monitoring. Several studies in the areas of Informative Frequency Selection bands (IFB) and Feature Extraction/Selection have demonstrated the need to identify the bands of interest in vibration signals. However, typical methods in these areas focus on identifying bands where impulsive excitations are present or analyzing the relevance of features after signal extraction. Therefore, they do not focus on other regions that may be related to changes in the dynamic behavior of machines or faults. Furthermore, the methods generally require parameter adjustments and are not automatic. To overcome these problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. In other words, automatically identify all frequency bands that are related to changes in the behavior of the machines or faults. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated on a synthetically dataset and on two real datasets, showing that BRF is capable of automatically identifying bands that present relevant information for the analysis of rotating machinery.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.