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A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-gaussian mixture model

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Chinese Journal of Chemical Engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong Yang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-gaussian distribution of blast furnace ***, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis gaussian mixture model Process monitoring Ensemble Process control

RBMDO Using gaussian mixture model-Based Second-Order Mean-Value Saddlepoint Approximation

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Computer modeling in Engineering & Sciences 2022年第8期132卷 553-568页

作者： Debiao Meng Shiyuan Yang Tao Lin Jiapeng Wang Hengfei Yang Zhiyuan Lv School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu611731China Failure Mechanics&Engineering Disaster Prevention and Mitigation Key Laboratory of Sichuan ProvinceSichuan UniversityChengdu610065China Yangzhou Yangjie Electronic Technology Co. Ltd.Yangzhou225008China Sichuan Special Equipment Inspection and Research Institute Chengdu610100China Food Safety Inspection Technology Center of Administration for Market Regulation of Sichuan Province Chengdu610017China

Actual engineering systems will be inevitably affected by uncertain ***,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system *** Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in ***,the SOMVSA method can only be used efficiently when the distribution of input variables is gaussian distribution,which significantly limits its *** this study,the gaussian mixture model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above *** is integrated with the Collaborative Optimization(CO)method to solve RBMDO ***,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are ***,an engineering example is given to show the application of the GMM-SOMVSA-CO method.

关键词： Uncertain factors reliability-based multidisciplinary design optimization saddlepoint approximate gaussian mixture model collaborative optimization

An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the gaussian mixture model and its application

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维普期刊数据库

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Petroleum Exploration and Development 2022年第2期49卷 339-348页

作者： GE Xinmin XUE Zong’an ZHOU Jun HU Falong LI Jiangtao ZHANG Hengrong WANG Shuolong NIU Shenyuan ZHAO Ji’er School of Geosciences China University of PetroleumQingdao 266580China CNPC Key Well Logging Laboratory Xi’an 710077China Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China Oil and Gas Survey Center of China Geological Survey Beijing 100083China China Petroleum Logging Co.Ltd. Xi’an 710077China PetroChina Research Institute of Petroleum Exploration&Development Beijing 100083China PetroChina Qinghai Oilfield Company Dunhuang 736202China Zhanjiang Branch of CNOOC Ltd. Zhanjiang 524057China Sinopec Research Institute of Petroleum Engineering Beijing 100101China

To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

关键词： NMR T2 spectrum gaussian mixture model expectation-maximization algorithm Akaike information criterion unsupervised clustering method quantitative pore structure evaluation

gaussian mixture model clustering with completed likelihood minimum message length criterion

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Journal of Southeast University(English Edition) 2013年第1期29卷 43-47页

作者：曾洪卢伟宋爱国东南大学仪器科学与工程学院南京210096 南京农业大学工学院南京210031

An improved gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

关键词： gaussian mixture model non-gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion

Multipath mitigation method based on gaussian mixture model in RF relative measurement

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Chinese Journal of Aeronautics 2018年第12期31卷 2270-2279页

作者： Weiqing MU Rongke LIU Zijie WANG Xinxin YANG School of Electronic and Information Engineering Beihang University

Radio Frequency(RF) technology represents a high-precision relative navigation solution that has significant potential for application to earth-orbiting satellites. In precision applications, multipath errors dominate the total error because observables, which are used to estimate carrier-phase integer ambiguity, are not always subject to a gaussian distribution when dualfrequency ambiguity estimation methods are used in the presence of multipath. As it has been shown that ranging observables obey a gaussian mixture distribution, this study proposes improvements to the accuracy of estimation based on multipath mitigation founded on the gaussian mixture model. To this end, such a model is created for integer ambiguity resolution in the presence of multipath, using which the theoretical error in dual-frequency ambiguity estimation is *** Maximization(EM), which aids dual-frequency ambiguity estimation, is subsequently proposed to reduce the effect of multipath errors. Finally, two experimental scenarios are implemented to test the performance of the proposed method. The results show that EM-aided dualfrequency ambiguity estimation reduces the range error to approximately 20% in comparison with simple dual-frequency ambiguity estimation. Therefore the proposed technique is effective for multipath mitigation in RF relative measurement.

关键词： Dual-frequency ambiguity estimation gaussian mixture model Multipath mitigation RF relative measurement Satellite navigation

Application of a Novel Method for Machine Performance Degradation Assessment Based on gaussian mixture model and Logistic Regression

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Chinese Journal of Mechanical Engineering 2011年第5期24卷 879-884页

作者： LIU Wenbin ZHONG Xin LEE Jay LIAO Linxia ZHOU Min Chemical Safety Engineering Research Center of Ministry of Education Beijing University of Chemical Technology Beijing 100029 China The First Research Institute of Ministry of Public Security Beijing 100048 China National Science Foundation Industry/University Cooperative Research Center of Intelligent Maintenance Systems University of Cincinnati Cincinnati OH 45221 USA China National Petroleum Corporation Beijing 100007 China

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different ***,failure data are always hard to acquire,thus making those techniques hard to be *** this paper,a novel method which does not need failure history data is *** packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and gaussian mixture model（GMM） is then applied to approximate the feature space ***-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of ***,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also *** SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine *** method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this *** proposed method has engineering significance for machine performance degradation assessment.

关键词： performance degradation assessment gaussian mixture model logistic regression proactive maintenance sensor fusion

Multimodal process monitoring based on transition-constrained gaussian mixture model

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Chinese Journal of Chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring gaussian mixture model State transition matrix Process control Process systems Systems engineering

Multimodal Biometric Score Fusion Using gaussian mixture model and Monte Carlo Method

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Journal of Computer Science & Technology 2010年第4期25卷 771-782页

作者： R Raghavendra Rao Ashok G Hemantha Kumar Department of Studies in Computer Science University of Mysore Department of E&C Channabasaveshwara Institute of Technology

Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on gaussian mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.

关键词： multimodal biometric system match score level fusion gaussian mixture model Monte Carlo method.

Short-term traffic safety forecasting using gaussian mixture model and Kalman filter

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第4期14卷 231-243页

作者： Sheng JIN Dian-hai WANG Cheng XU Dong-fang MA College of Civil Engineering and Architecture Zhejiang University Zhejiang Police College

In this paper,a prediction model is developed that combines a gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on *** time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe ***,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) *** propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance *** test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different *** results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

关键词： Forecasting Traffic safety gaussian mixture model Kalman filter