{"title":"改进型扩展经验小波变换,用于全厂工业控制回路的精确多变量振荡检测和特征描述","authors":"Wahiba Bounoua, Muhammad Faisal Aftab","doi":"10.1016/j.jprocont.2024.103226","DOIUrl":null,"url":null,"abstract":"<div><p>The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops\",\"authors\":\"Wahiba Bounoua, Muhammad Faisal Aftab\",\"doi\":\"10.1016/j.jprocont.2024.103226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.</p></div>\",\"PeriodicalId\":50079,\"journal\":{\"name\":\"Journal of Process Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Process Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0959152424000660\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152424000660","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops
The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.