Industrial Big Data Research Articles

Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

Виклики фінансової глобалізації для розвитку територій України: напрями мінімізації та регулювання

The active development of digital technologies, in particular Internet banking, algorithmic trading, big data industry, credit electronic platforms, blockchain and cryptocurrencies with a gradual abandonment of cash payments, causes a radical transformation of the financial system of most countries and creates new challenges in monetary lending and increases financial imbalances in the regional development of Ukraine. The article is aimed at deepening the theoretical and applied bases of the study of the mutual relationship between the challenges of globalization and financial imbalances in regional development, substantiating the main directions of their regulation, and minimizing the negative consequences of financial globalization. The article defines the need for a radical transformation of Ukraine’s regional financial systems and the development of a new policy to counteract the challenges of globalization in order to overcome financial imbalances in the regions of the country, namely: clear separation of powers of the subjects of regional regulation of the cryptocurrency market; implementation of financial regional monitoring and control over the subjects of primary financial monitoring; development of digital infrastructure, creation of conditions for the implementation of network segmentation technologies; retraining and social adaptation of financial workers due to the active development of information technologies. Counteracting financial crime, money laundering, cross-border and international crime is a key component in overcoming financial imbalances in the development of Ukrainian regions. It is proved that the introduction of a comprehensive system of minimizing the manifestations of shadowing the monetary sector will have positive results in the context of strengthening financial security.

Computer system for resource-saving control of multi-assortment production of polymeric materials based on processing big industrial data

Разработана перенастраиваемая компьютерная система управления многоассортиментным гибким производством полимерных материалов, которая формирует советы оператору по ресурсосберегающему управлению на основе обработки больших массивов промышленных данных с использованием методов интеллектуального анализа данных и математического моделирования. Она настраивается на выпуск различного ассортимента продукции, различные конфигурации производственных линий и требования к производительности и качеству продукции. Компьютерная система состоит из следующих основных модулей: - сбор контролируемых параметров производства; - расчет неконтролируемых на производстве показателей качества полупродуктов (экструдата) и целевой продукции; - обработка массивов измеренных и рассчитанных параметров производства для прогнозирования потребительских характеристик продукции и формирования советов по управлению производством. Включенные в состав системы математические модели, выступая в качестве «виртуальных анализаторов», позволяют рассчитывать в реальном времени индекс термической деструкции, степень смешения и цветовые характеристики экструдата; разнотолщинность, степень усадки и цветовые характеристики полимерного материала. Для прогнозирования потребительских характеристик продукции применяются модули многофакторного регрессионного анализа производственных данных (при нормальном распределении данных) и модули машинного обучения с использованием искусственных нейронных сетей и адаптивного композиционного усиленного метаалгоритма (если распределение данных отклоняется от нормального). Визуализация данных осуществляется в виде трендов и трехмерных графиков с отображением рациональных значений управляющих воздействий, обеспечивающих требуемое качество продукции. Применение системы в качестве советчика операторов при определении ресурсосберегающих режимов производства позволяет сократить время принятия управленческих решений, повысить производительность, уменьшив брак полимерных материалов.

Sustainable Industrial Big Data, Automated Production Processes, and Smart Networked Factories in Cyber-Physical System-based Manufacturing

Sustainable Industrial Big Data, Automated Production Processes, and Smart Networked Factories in Cyber-Physical System-based Manufacturing

Smart Factory Performance, Cognitive Automation, and Industrial Big Data Analytics in Sustainable Manufacturing Internet of Things

Smart Factory Performance, Cognitive Automation, and Industrial Big Data Analytics in Sustainable Manufacturing Internet of Things

Automatic Identification of Bottleneck Tasks for Business Process Management using Fusion-based Text Clustering

Abstract With the arrival of the industrial big data era, it offers unprecedented opportunities for machine learning to intelligently uncover hidden tasks and restore the entire underlying process for business process modelling. While recent studies, e.g., process mining and ontologies, have advanced the research agenda of business process modelling and management, identifying a bottleneck task automatically needs more in-depth research. In this paper, a text mining-based bottleneck task identification approach is proposed. Firstly, to extract tasks from documents in different lengths, a dynamic sliding window is introduced to the biterm topic model. The sliding window size is adjusted according to document length during biterm selection process to ensure the two words in biterm comes from a context. Secondly, a fusion-based clustering algorithm is studied to uncover business tasks. The improved biterm topic model and the Doc2vec model are used to train two document vectors and then calculate two distances. The linear fusion of these two distances is used as the metric of clustering. Thirdly, the temporal frequency of each task at different periods is calculated to show the timeline and abnormal occurrence of tasks to identify bottleneck tasks. The proposed approach is evaluated using a data set containing the execution of a multi-year multidisciplinary student design project. The experiment results show the approach can effectively identify bottleneck tasks without manual intervention.

Design and implementation of a semantic gateway based on SSN ontology

Abstract The massive data in the industrial big data field mainly comes from a large number of sensors. Sensor data is discrete numerical data with heterogeneity. In scenarios such as edge computing, it is often necessary to mine the relationships between the data in the data stream so as to realize the full use of heterogeneous sensor data. Because the sensor data does not contain contextual semantic relations, the relationship between the data is difficult to be discovered. Aiming at the above shortcomings, we design and implement a semantic gateway with semantic annotation function. The original sensor data will become RDF data with semantic information after being processed by the semantic gateway. Our work is mainly divided into two aspects: according to the source and actual characteristics of the data, we choose to carry out the semantic work based on the SSN ontology; next, we propose an OB2R algorithm to realize the semantic annotation function of the data, and the correctness of the algorithm is verified on the PHM2010 dataset.

Internet of Things-based Decision Support Systems, Industrial Big Data Analytics, and Autonomous Production Processes in Sustainable Smart Manufacturing

Internet of Things-based Decision Support Systems, Industrial Big Data Analytics, and Autonomous Production Processes in Sustainable Smart Manufacturing

The design of adaptive communication frame supporting high-speed transmission based on ModBus protocol

Abstract In the era of industrial big data, the demand for high-speed data transmission is higher and higher. The traditional Modbus protocol uses fixed data frame length transmission, which can not make full use of link resources, resulting in very low transmission rate. In the early stage, in order to solve the problem of traditional ModBus transmission protocol, an algorithm was proposed to adjust the frame length by calculating the optimal frame length through the frame error rate. However, because the frame length of each data frame is adjusted in this algorithm, the link stability becomes worse and the efficiency is reduced. In order to solve this problem, this paper proposes an improved adaptive frame length adjustment algorithm, which adjusts the frame length according to the retransmission failure rate per unit time, and gives different adjustment schemes according to different situations, which improves the link stability. Finally, this paper takes the servo drive data acquisition system as the carrier, and verifies the effectiveness of the algorithm through experiments.

Analysis and Research Based on Instrument Drift Data

Instruments and meters are used to inspect, measure, control, analyze, calculate and display the physical quantity, chemical quantity, biomass, electrical parameters, geometric quantity, and movement status of the object under test. Its applications cover a wide range of industries, agriculture, transportation, science, and technology, etc., and can be divided into electrical instruments, optical instruments, laboratory instruments, analytical instruments, testing machines, industrial instruments, etc. It assumes the task of gatekeeper and guide in the construction of the national economy. The analysis and research techniques of this article are based on the professional data of power plant industrial instrumentation. This paper presents an overview of various analysis methods of instrument data, and in-depth analysis of time correlation and coverage analysis, outlier analysis, etc. We give the statistical analysis process of drift data, focusing on the process of the AFAL method; We also described the drift algorithm of random behavior and deviation behavior and explored and discussed the analysis tools and data analysis results of the power plant industry professional data. We have designed a set of application systems for instrument data import, export, storage, and analysis, which can store instrument calibration data onto a Hadoop database in a prescribed format. Which provides great reference values for the calibration of instrument data. Finally, it looks forward to the analysis and research direction and development trend of industrial big data.

Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories

Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories

Sustainable Industrial Big Data, Automated Production Processes, and Cyber-Physical System-based Manufacturing in Smart Networked Factories

Sustainable Industrial Big Data, Automated Production Processes, and Cyber-Physical System-based Manufacturing in Smart Networked Factories

Augmented Multidimensional Convolutional Neural Network for Industrial Soft Sensing

In the era of industrial big data, data-driven soft-sensor models have become an important method to guide production and optimize control. However, due to the limitation of data acquisition methods, the industrial data obtained in practice have unavoidable defects that affect the performance of soft sensors. Aiming at the unbalanced sampling, inaccurate matching, and partial missing of industrial process data, this article proposes an augmented multidimensional convolutional neural network (CNN) for industrial soft sensing. For complete process data information, we stitch the fine-grained data to obtain coarse-grained data and then use a CNN to extract deep features. On the basis of analyzing the physical meaning of data, the structure of multidimensional convolution is designed to focus on different details of process information. In this framework, the problem of partial missing data is emphasized. Then, mix-grained data augmentation strategies are invented to solve this problem and improve the performance of multidimensional CNN (MDCNN) soft sensors. The proposed augmented MDCNN is applied to the f-CaO prediction of the cement clinker production process, results of which show superiority compared to existing methods.

Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

The prognostic and health management (PHM) of rolling bearings has been a popular research area. Since bearing fault is inevitable during degradation, how to improve the PHM performance based on both degradation states and fault types is still an open problem. In this study, two multilabel learning algorithms are proposed for PHM of rolling bearings, named personalized binary relevance (PBR) and hierarchical multilabel K-nearest neighbor (HML-KNN), respectively. Degradation states and fault types are used as the labels of the bearing data so that each sample has a corresponding label sequence, that is to say, the PHM problem is converted to a multilabel learning problem. Both algorithms have a personalized search process, which can not only help samples build a personalized model to improve classification accuracy but also solve the problem of data imbalance between labels. At the same time, the two algorithms also have their own characteristics and focus on different application situations. The PBR algorithm has faster modeling speed, more flexible use, and replaceable subclassifiers. HML-KNN is a high-order algorithm with global information analysis capabilities through the hierarchical processing of data and the conversion of label information. Both methods have achieved good enough results in the XJTU-SY bearing dataset. In order to illustrate the practicality of the algorithm, the experimental part further increases the difficulty. Using only a single faulty sample as the training set to determine the type of compound fault, the algorithms also show high performance. In the era of industrial big data, the depth of data mining and the design of algorithm models will help us better manage equipment health.

Cyber-Physical System-based Real-Time Monitoring, Industrial Big Data Analytics, and Smart Factory Performance in Sustainable Manufacturing Internet of Things

Cyber-Physical System-based Real-Time Monitoring, Industrial Big Data Analytics, and Smart Factory Performance in Sustainable Manufacturing Internet of Things

Internet of Things Smart Devices, Sustainable Industrial Big Data, and Artificial Intelligence-based Decision-Making Algorithms in Cyber-Physical System-based Manufacturing

Internet of Things Smart Devices, Sustainable Industrial Big Data, and Artificial Intelligence-based Decision-Making Algorithms in Cyber-Physical System-based Manufacturing

Analysis of the Influence Path of Guiyang Big Data Expo on Regional Economic Developments

Exhibition industry is an important part of modern service industry, and exhibition economy can play a strong driving role in regional and industrial development. At the same time, the exhibition economy is different from other industries. In addition to the direct benefits generated by the exhibition itself, the exhibition industry has a strong pulling effect on the regional economic development due to its strong clustering characteristics. The Big Data Expo is an important platform for the development of national big data industry, through the development of Guiyang Digital Expo, this paper proposes to promote the optimization and upgrading of regional industrial structure by cultivating exhibition industry chain, promoting the evolution of regional economic development path and enhancing regional comprehensive competitiveness, so as to realize the driving effect of exhibition industry and exhibition economy on regional economic development.

Adaptive process monitoring via online dictionary learning and its industrial application

For industrial processes, one common drawback of conventional process monitoring methods is that they would make an increasing number of false alarms in cases of various factors such as catalyst deactivation, seasonal fluctuation and so forth. To address this issue, the present work proposes an online dictionary learning method, which can fulfill the process monitoring and fault diagnosis task adaptively. The proposed method would incorporate currently available information to update the dictionary and control limit, instead of keeping a fixed monitoring model. The online dictionary learning method are more superior than conventional methods. Firstly, compared with some traditional offline methods based on small amounts of historical data, the proposed method can augment train data with online dictionary updating, thus it copes with time-varying processes well. Secondly, the proposed method enjoys a lower computational complexity than the offline learning method with mass data, which is appealing in the era of industrial big data. Thirdly, the proposed method performs more reliably than the existing recursive principal component analysis-based methods because it can resolve the anomaly of principal component or non-orthogonality of eigenvectors problem which was often confronted in the recursive principal component analysis-based methods. Finally, some experiments were designed and carried out to demonstrate the advantage of the online dictionary learning.

The Titan Control Center for Industrial DevOps analytics research

<h2>Abstract</h2> The Titan Control Center is a software platform supporting research on industrial big data analytics. Building upon a scalable and extensible architecture, the Titan Control Center analyzes and visualizes data streams from Internet of Things sensors in industrial production. It performs different types of aggregations, correlation, forecasting, and anomaly detection to provide deeper insights into industrial production data for enabling Industrial DevOps. Furthermore, the Titan Control Center is used in research for implementing and evaluating novel approaches on multi-dimensional sensor data stream aggregation, as a reference platform for benchmarking scalability in modular analytics software, and for research on analyzing industrial energy consumption.

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series.

In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.