Big Data Analysis of Manufacturing Processes

Windmann, Stefan; Maier, Alexander; Niggemann, Oliver; Frey, Christian W.; Bernardi, Ansgar; Gu, Ying; Pfrommer, H.; Steckel, Thilo; Krüger, Michael; Kraus, Robert

doi:10.1088/1742-6596/659/1/012055

Cited by 37 publications

(15 citation statements)

References 12 publications

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“…Through this statistic, it can be shown that the researches of big-data based solutions focus on monitoring, prediction, data analytics and propose ICT solutions in manufacturing in Table 3. [57], [56], [125], Regressions [53], ANN [53], PLM [58], Production ERP MP [63], regression [62], K-means [126] MOM/MES [69], [116], [66], regression [127], Distance, Regression, Selforganizing map, principal component analysis [128], SCADA/DCS/ HMI [129], [71], [104], [130], [131], [104], [132] , [112] Classification [133], OPL [106], KM [134], GA [73], O&M [39], [135] [136], [81], [82], [80] logistic regression, naïve Bayes, and a decision tree [109], regression [137], LSM [138], SVM [83], Anomaly detection [139], DTW [140], RF [141], K-means, Markov [142], KD [24],…”

Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

“…It is concluded that information integration from the web and other data sources is a critical issue to implement system collaboration [153]. Processing and analysing data from MES and SCADA with MapReduce and BDA can detect anomaly minutes beforehand in a large-scale production [128]. In the industry, Bosch presented a conceptual, analytic platform with a data integration method to integrate various data sources [154].…”

Section: Driver 1: System Integrationmentioning

confidence: 99%

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Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

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Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

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Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

Section: Driver 1: System Integrationmentioning

confidence: 99%

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

View full text Add to dashboard Cite

show abstract

“…On the other hand, techniques based on AI such as machine learning (ML) are a possibility to analyse information in the absence of models. ML algorithms in CPPS have been addressed in [87,88] using ML techniques for the automatic detection of anomalous and sub-optimal plant situations. Features like AI or ML are of capital importance when analysing the large amounts of unstructured and heterogeneous data produced by the pervasive availability of information in CPPS.…”

Section: Data Analysis: This Cluster Discusses How Informationmentioning

confidence: 99%

From a literature review to a conceptual framework of enablers for smart manufacturing control

Rojas

Rauch

2019

Int J Adv Manuf Technol

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When cyber-physical systems (CPS) are connected to perform smart manufacturing, we have a cyber-physical production system (CPPS). In such systems, CPS collaborate in an intelligent way to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. This work, based on a systematic literature review, shows current trends in cyber-physical production systems with a special focus on the role of connectivity and control systems in production. We are looking for the enablers of CPPS focusing on the current evolution of control systems in the smart factory of the future. As a result, a conceptual framework as well as a CPPS control system architecture for the introduction and organisation of enablers for CPPS is presented to support practitioners in the realisation of smart manufacturing control concepts.

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“…Physical properties of the process are constantly monitored, often polling data every few milliseconds in the case of critical variables, which with large, continuous processes can lead to a scenario where it is necessary to use Big Data Analytics (BDA), covered in Section 1, in order to process field and control data. This is further confirmed by proposals that, outside the field of security research, point to this need and propose several BDA solutions focused on industrial applications, such as process monitoring [51][52][53][54], maintenance [55], fault detection [56], and fault diagnosis [57,58].…”

Section: Anomaly Detection Systemsmentioning

confidence: 66%

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Iturbe

Garitano

Zurutuza

et al. 2017

Security and Communication Networks

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Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community. While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of Big Data frameworks such as Hadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security, mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing IN-based ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development.

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Big Data Analysis of Manufacturing Processes

Cited by 37 publications

References 12 publications

Manufacturing big data ecosystem: A systematic literature review

Manufacturing big data ecosystem: A systematic literature review

From a literature review to a conceptual framework of enablers for smart manufacturing control

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

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