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Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling 被引量:3
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作者 Changkyoo Yoo Minhan Kim Sunjin Hwang Yongmin Jo Jongmin Oh 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2008年第1期48-51,共4页
A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling... A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods. 展开更多
关键词 inferential sensing multiway modeling non-Gaussian distribution online predictive monitoring process supervision wastewater treatment process
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Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory 被引量:1
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作者 杨建文 陈祥光 金怀平 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第6期1009-1016,共8页
Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is conta... Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency. 展开更多
关键词 ChlortetTacycline fermentation online prediction of contamination Dezert-Smarandache theory Self-recursive wavelet neural network Unscented kalman filter
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Online prediction of EEG based on KRLST algorithm
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作者 Lian Zhaoyang Duan Lijuan +2 位作者 Chen Juncheng Qiao Yuanhua Miao Jun 《High Technology Letters》 EI CAS 2021年第4期357-364,共8页
Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simp... Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simple in time series.The distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive algorithms.What’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG signals.The time series prediction of EEG plays an important role in EEG time series analysis.In this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms.The experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset. 展开更多
关键词 brain computer interface(BCI) kernel adaptive algorithm online prediction of electroencephalograph(EEG)
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A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries 被引量:2
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作者 Xinyan Liu Xue-Qiang Zhang +4 位作者 Xiang Chen Gao-Long Zhu Chong Yan Jia-Qi Huang Hong-Jie Peng 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2022年第5期548-555,共8页
Estimating battery degradation is vital not only to monitor battery’s state-of-health but also to accelerate research on new battery chemistries. Herein, we present a data-driven approach to forecast the capacity fad... Estimating battery degradation is vital not only to monitor battery’s state-of-health but also to accelerate research on new battery chemistries. Herein, we present a data-driven approach to forecast the capacity fading trajectory of lab-assembled lithium batteries. Features with physical meanings in addition to predictive abilities are extracted from discharge voltage curves, enabling online prediction for a single cell with only its historical data. The robustness and generalizability allow for the demonstration on a compromised quality dataset consisting of batteries varying in battery architectures and cycling conditions,with superior accuracy for end of life and degradation trajectory prediction with average errors of 8.2%and 2.8%, respectively. Apart from the impressive prediction accuracy, the as-extracted features also provide physical insights, the incorporation of which into material design or battery operation conditions further enlightens the development of better batteries. We highlight the effectiveness of time-seriesbased techniques in forecasting battery cycling performance, as well as the huge potential of datadriven methods in unveiling hidden correlations in complicated energy chemistries such as lithium metal batteries. 展开更多
关键词 Battery prognosis Machine learning Time series forecasting online prediction Lithium metal batteries
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Adaptive online prediction method based on LS-SVR and its application in an electronic system 被引量:2
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作者 Yang-ming GUO Cong-bao RAN Xiao-lei LI Jie-zhong MA 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第12期881-890,共10页
Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or accept... Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time. 展开更多
关键词 Adaptive online prediction Least squares support vector regression(LS-SVR) Electronic system
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