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performance monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

Chinese Journal of Chemical Engineering 2006年第2期14卷 207-215页

作者：张强李少远 Institute of Automation Shanghai Jiao Tong University Shanghai 200240 China

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

关键词： predictive control performance monitoring diagnosis principal component analysis

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Optimal Machine Learning Enabled performance monitoring for Learning Management Systems

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Computer Systems Science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled performance monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Application of Kernel GDA to performance monitoring and Fault Diagnosis for Rotating Machinery

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Journal of Donghua University(English Edition) 2010年第5期27卷 709-714页

作者：马思乐张曦邵惠鹤 School of Control Science and Engineering Shandong University Guangdong Electric Power Research Institute School of Electronic Information and Electrical EngineeringShanghai Jiaotong University

Faults in rotating machine are difficult to detect and identify,especially when the system is complex and *** order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was *** KGDA,the data were mapped from the original space to the high-dimensional feature *** the statistic distance between normal data and test data was constructed to detect whether a fault was *** a fault had occurred,similar analysis was used to identify the type of *** effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.

关键词： kernel generalized discriminant analysis(KGDA) performance monitoring fault diagnosis rotating machinery

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Lossy nodes inference based on end-to-end passive monitoring in wireless sensor networks

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High Technology Letters 2011年第4期17卷 388-394页

作者： Yang Yu Xu Yongjun Li Xiaowei Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P. R. China Graduate University of Chinese Academy of Sciences Beijing 100039 P. R. China

This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth constraints, a space-efficient packet marking scheme is first introduced. The scheme uses a Bloom filter as a compression tool so that path information can bc piggybacked by data packets. Based on the path information, LoNI then adopts a fast algorithm to detect lossy nodes. The algorithm formulates the inference problem as a weighted set-cover problem and solves it using a greedy approach with low complexity. Simulations show that LoNI can locate about 80% of lossy nodes when lossy nodes are rare in the network. Furthermore, LoNI performs better for the lossy nodes near the sink or with higher loss rates.

关键词： wireless sensor networks(WSNs) performance monitoring lossy nodes Bloom filter weighted set-cover

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Assessment of the Efficiency of a Mewis Duct Through performance Analysis of Operational Data

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Journal of Marine Science and Application 2022年第2期21卷 144-158页

作者： Anastasios Zalachoris Nikos Themelis School of Naval Architecture and Marine Engineering National Technical University of Athens 9 Iroon Polytechneiou15780AthensGreece

Τhe efficiency of a Mewis propeller duct by the analysis of ship operational data is *** analysis employs data collected with high frequency for a three-year period for two siter vessels,one of them fitted with a Mewis type *** approach to the problem of identifying improvements in the operational performance of the ship equipped with the duct is ***,we proceed with the calculation of appropriate Key performance Indicators to monitor vessels performance in time for different operational periods and loading *** extensive pre-processing stage is necessary to prepare a dataset free from datapoints that could impair the analysis,such as outliers,as well as the appropriate preparations for a meaningful KPI *** second approach concerns the development of multiple linear regression problem for the prediction of main engine fuel oil consumption based on operational and weather parameters,such as ship’s speed,mean draft,trim,rudder angle and the wind *** aim is to quantify reductions due to the Mewis duct for several *** results of the studies reveal a contribution of the Mewis duct mainly in laden condition,for lower speed range and in the long-term period after dry-docking.

关键词： performance monitoring Energy saving device Regression models Data processing Mewis duct

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Gold-antimony Flotation Recognition Based on Lightweight Sparse Attention Depth Networks

Gold-antimony Flotation Recognition Based on Lightweight Spa...

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第43届中国控制会议

作者： Tian Li Yawen Xiao Peng Xie Xinyi Duan Yaoyi Cai College of Engineering and Design Hunan Normal University

In froth flotation, accurate, real-time identification of working conditions is highly important for industrial ***, the current manual observation methods used in froth flotation suffer from high subjectivity and errors. Therefore, a method for identifying gold-antimony flotation work conditions based on a lightweight sparse attention depth network is proposed. The proposed network is mainly composed of the transformer encoder module in VIT and the inverted residual block in MobilenetV2. This network combining the transformer and CNN can better preserve global and local features. The ECA module is also introduced to reduce the redundancy of information between channels. The experimental results show that the proposed algorithm has an average accuracy of 97.17% on the self-built dataset of gold antimony flotation images, which is significantly higher than that of classical convolutional neural networks such as AlexNet, VGG16, ResNet18, DenseNet,MobileNetV2, and EfficientNet;the number of parameters of the proposed algorithm is 5.485 M. In addition, the visualization of the results shows that the network is able to distinguish the gold-antimony flotation condition categories well. In summary,the network has few parameters and can provide an effective reference for identifying gold-antimony flotation conditions.

关键词： deep learning froth flotation transformer performance monitoring attention mechanism

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Applications of Adaptive Genetic Algorithm to Radar Engine Fault Diagnosis

Applications of Adaptive Genetic Algorithm to Radar Engine F...

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2010 Chinese Control and Decision Conference

作者： Zhang Peng~a,Pan Wei~a,Zhu Lma~b,Wang Dezhi~a a.Electric Detection Department b.Fundamental Department,Shenyang Artillery Academy,Shenyang 110867,China

＜正＞According to the problem on calculating the synthetic exponent characterizing the whole performance of radar engine by using the synthetic weighted method,the weights of every parameter are difficult to be *** solve this problem,a method of determining the weights of every parameter by adaptive genetic algorithm is *** synthetic exponent gained by AGA is more sensitive and exact than the one gained by the expert investigated method in reflecting the whole performance of the engines. Mean while,this method improves the rate of identifying whether the performance of the engine is normal or not,finds the potential forepart fault of engine and prevents the spread of the *** validity of the method is testified by monitoring certain type of turbine-fan *** adaptive crossover probability and adaptive mutation probability are proposed,which consider the influence of every generation to algorithm and the effect of different individual fitness in every generation.

关键词： Radar Engine performance monitoring Fault Diagnosis Adaptive Genetic Algorithm Crossover Probability Mutation Probability

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