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Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2358-2367页

作者： Cen Guo Wenkai Hu Fan Yang Dexian Huang Department of Automation Tsinghua UniversityBeijing 10084China Cornell University NY 14850United States of America University of Alberta EdmontonAB T6G 1H9Canada

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

关键词： Alarm configuration Deep learning Fault detection and diagnosis incomplete data Stacked autoencoder

Belief Combination of Classifiers for incomplete data

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

Bayesian estimation of a power law process with incomplete data

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Journal of Systems Engineering and Electronics 2021年第1期32卷 243-251页

作者： HU Junming HUANG Hongzhong LI Yanfeng School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Transportation and Automotive Engineering Xihua UniversityChengdu 610039China Center for System Reliability and Safety University of Electronic Science and Technology of ChinaChengdu 611731China

Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable *** statistical inference on the parameters of the power law process has been well developed,numerous studies largely depend on complete failure data.A few methods on incomplete data are reported to process such data,but they are limited to their specific cases,especially to that where missing data occur at the early stage of the *** framework to handle generic scenarios is *** overcome this problem,from the point of view of order statistics,the statistical inference of the power law process with incomplete data is established in this *** theoretical derivation is carried out and the case studies demonstrate and verify the proposed *** statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified *** results show that the proposed method has more flexibility and more applicability.

关键词： incomplete data power law process Bayesian inference order statistics repairable system

Energy Consumption Prediction of a CNC Machining Process With incomplete data

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 987-1000页

作者： Jian Pan Congbo Li Ying Tang Wei Li Xiaoou Li the State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing 400044China the Department of Electrical and Computer Engineering Rowan UniversityGlassboroNJ 08028 USA the Computer Science Department The Research and Advanced Studies Centre of the National Polytechnic InstituteMexico City 07360Mexico

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization *** improve the generalization abilities,more and more parameters are acquired for energy prediction *** the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,*** work proposes a framework for energy modeling based on incomplete data to address this ***,some necessary preliminary operations are used for incomplete data ***,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data ***,we test the predictive accuracy of the obtained *** experiments are designed to investigate the performance of the proposed framework with different rates of missing *** results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

关键词： Energy consumption prediction incomplete data generative adversarial imputation nets(GAIN) gene expression programming(GEP)

Analysis of incomplete data of Accelerated Life Testing with Competing Failure Modes

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Chinese Journal of Mechanical Engineering 2009年第6期22卷 883-889页

作者： TAN Yuanyuan ZHANG Chunhua CHEN Xun College of Mechatronics Engineering and Automation National University of Defense Technology Changsha 410073 China

data obtained from accelerated life testing （ALT） when there are two or more failure modes, which is commonly referred to as competing failure modes, are often incomplete. The incompleteness is mainly due to censoring, as well as masking which might be the case that the failure time is observed, but its corresponding failure mode is not identified. Because the identification of the failure mode may be expensive, or very difficult to investigate due to lack of appropriate diagnostics. A method is proposed for analyzing incomplete data of constant stress ALT with competing failure modes. It is assumed that failure modes have s-independent latent lifetimes and the log lifetime of each failure mode can be written as a linear function of stress. The parameters of the model are estimated by using the expectation maximum （EM） algorithm with incomplete data. Simulation studies are performed to check＇model validity and investigate the properties of estimates. For further validation, the method is also illustrated by an example, which shows the process of analyze incomplete data from ALT of some insulation system. Because of considering the incompleteness of data in modeling and making use of the EM algorithm in estimating, the method becomes more flexible in ALT analysis.

关键词： accelerated life testing competing failure modes expectation maximum algorithm incomplete data Monte Carlo simulation

A Fast and Effective Multiple Kernel Clustering Method on incomplete data

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Computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

Efficient k-dominant skyline query over incomplete data using MapReduce

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Frontiers of Computer Science 2021年第4期15卷 151-164页

作者： Linlin DING Shu WANG Baoyan SONG School of Information Liaoning UniversityShenyang 110036China

Skyline queries are extensively incorporated in various real-life applications by filtering uninteresting data ***,a skyline query may return so many results because it cannot control the retrieval conditions especially for highdimensional *** an extension of skyline query,the kdominant skyline query reduces the control of the dimension by controlling the value of the parameter k to achieve the purpose of reducing the retrieval *** addition,with the continuous promotion of Bigdata applications,the data we acquired may not have the entire content that people wanted for some practically reasons of delivery failure,no power of battery,accidental loss,so that the data might be incomplete with missing values in some ***,the k-dominant skyline query algorithms of incomplete data depend on the user definition in some degree and the results cannot be ***,the existing algorithms are unsuitable for directly used to the incomplete big *** on the above situations,this paper mainly studies k-dominant skyline query problem over incomplete dataset and combines this problem with the distributed structure like MapReduce ***,we propose an index structure over incomplete data,named incomplete data index based on dominate hierarchical tree(ID-DHT).Applying the bucket strategy,the incomplete data is divided into different buckets according to the dimensions of missing ***,we also put forward query algorithm for incomplete data in MapReduce environment,named MapReduce incomplete data based on dominant hierarchical tree algorithm(MR-ID-DHTA).The data in the bucket is allocated to the subspace according to the dominant condition by Map *** function controls the data according to the key value and returns the k-dominant skyline query *** effective experiments demonstrate the validity and usability of our index structure and the algorithm.

关键词： k-dominant skyline query incomplete data MapReduce index structure big data

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with incomplete data for Smart Grid in Smart Cities

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 527-554页

作者： Zeyu Wu Bo Sun Qiang Feng Zili Wang Junlin Pan School of Reliability and Systems Engineering Beihang UniversityBeijingChina Institute of Reliability Engineering Beihang UniversityBeijingChina

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart ***,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation *** article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting *** incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial *** augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind ***,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete *** incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s *** with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

关键词： Physics-informed method probabilistic forecasting wind power generative adversarial network extreme learning machine day-ahead forecasting incomplete data smart grids

Power incomplete data Clustering Based on Fuzzy Fusion Algorithm

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Energy Engineering 2023年第1期120卷 245-261页

作者： Yutian Hong Yuping Yan Guangdong Electric Power Information Technology Co. Ltd.Guangzhou520000China Guangdong Power Grid Co. Ltd.Guangzhou520000China

With the rapid development of the economy,the scale of the power grid is *** number of power equipment that constitutes the power grid has been very large,which makes the state data of power equipment grow *** multi-source heterogeneous data have data differences,which lead to data variation in the process of transmission and preservation,thus forming the bad information of incomplete ***,the research on data integrity has become an urgent *** paper is based on the characteristics of random chance and the Spatio-temporal difference of the *** to the characteristics and data sources of the massive data generated by power equipment,the fuzzy mining model of power equipment data is established,and the data is divided into numerical and non-numerical data based on numerical *** the text data of power equipment defects as the mining ***,the Apriori algorithm based on an array is used to mine *** strong association rules in incomplete data of power equipment are obtained and *** the change trend of NRMSE metrics and classification accuracy,most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend,and will not fluctuate greatly with the growth of the missing *** experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets,and the filling effect fluctuates greatly with the increase of the missing rate,that is,with the increase of the missing rate,the improvement effect of the model for the existing filling methods is higher than 4.3%.Through the incomplete data clustering technology studied in this paper,a more innovative state assessment of smart grid reliability operation is carried out,which has good research value and reference significance.

关键词： Power system equipment parameter incomplete data fuzzy analysis data clustering