{"title":"通过流向算法优化基于频谱峰度的滤波,实现早期故障检测","authors":"","doi":"10.1016/j.measurement.2024.115737","DOIUrl":null,"url":null,"abstract":"<div><p>This research focuses on developing a denoising filter that effectively enhances subtle non-stationarities within signals. Initially, the spectral kurtosis has been calculated from each frequency bin of the spectrogram (setting initial values) which is further optimized by the flow direction algorithm (FDA) to obtain a vector of optimized spectral kurtosis as the classical spectral kurtosis as a data-driven estimator may be not optimal in the sense of informative signal extraction for some specific signals with complex time–frequency structure. Due to this, the vectors obtained from optimized spectral kurtosis for a given frequency bin could be even zero if they do not support the impulsiveness of the output signal. In this way, it allows not to use any thresholding that is always complicated. The vector of the optimized spectral kurtosis thus obtained has been used to design the filter that can extract the impulsive components from the signal.</p></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of spectral kurtosis-based filtering through flow direction algorithm for early fault detection\",\"authors\":\"\",\"doi\":\"10.1016/j.measurement.2024.115737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This research focuses on developing a denoising filter that effectively enhances subtle non-stationarities within signals. Initially, the spectral kurtosis has been calculated from each frequency bin of the spectrogram (setting initial values) which is further optimized by the flow direction algorithm (FDA) to obtain a vector of optimized spectral kurtosis as the classical spectral kurtosis as a data-driven estimator may be not optimal in the sense of informative signal extraction for some specific signals with complex time–frequency structure. Due to this, the vectors obtained from optimized spectral kurtosis for a given frequency bin could be even zero if they do not support the impulsiveness of the output signal. In this way, it allows not to use any thresholding that is always complicated. The vector of the optimized spectral kurtosis thus obtained has been used to design the filter that can extract the impulsive components from the signal.</p></div>\",\"PeriodicalId\":18349,\"journal\":{\"name\":\"Measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0263224124016221\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263224124016221","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Optimization of spectral kurtosis-based filtering through flow direction algorithm for early fault detection
This research focuses on developing a denoising filter that effectively enhances subtle non-stationarities within signals. Initially, the spectral kurtosis has been calculated from each frequency bin of the spectrogram (setting initial values) which is further optimized by the flow direction algorithm (FDA) to obtain a vector of optimized spectral kurtosis as the classical spectral kurtosis as a data-driven estimator may be not optimal in the sense of informative signal extraction for some specific signals with complex time–frequency structure. Due to this, the vectors obtained from optimized spectral kurtosis for a given frequency bin could be even zero if they do not support the impulsiveness of the output signal. In this way, it allows not to use any thresholding that is always complicated. The vector of the optimized spectral kurtosis thus obtained has been used to design the filter that can extract the impulsive components from the signal.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.