Overview of the CPS for Smart Factories Project: Deep Learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces

Sonntag, Daniel; Zillner, Sonja; Smagt, Patrick van der; Lörincz, András

doi:10.1007/978-3-319-42559-7_19

Cited by 32 publications

(13 citation statements)

References 23 publications

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“…Deep learning where multiple layers have been employed in order to build an ANN, which is able to make intelligent decisions, handling large amounts of data with high complexity, without any human intervention [15,22,23]. Some DL algorithms are convolutional neural networks (CNNs), restricted Boltzmann machine (RBM) and auto-encoders (AE) [23].…”

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confidence: 99%

“…These include process monitoring and quality control, fault detection and diagnosis, as well as machine health monitoring and predictive maintenance [24]. Moreover, the capabilities of ML, regarding the timely processing of an abundance of data are critical to safeguarding the cyber-security of the Industrial Internet of Things (IIoT) enabled interconnected manufacturing environments, accurately detecting and mitigating threats [22,25].…”

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confidence: 99%

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Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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confidence: 99%

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confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

View full text Add to dashboard Cite

show abstract

“…Deep neural networks (DNNs) has been used in various applications of Artificial Intelligence (AI), computer vision and recognition models (Liu et al, 2017). Nowadays, these are extremely effective and flexible for extracting actionable, highlevel information from the raw data produced by a wide variety of sensors in CPSs (Sonntag, Zillner, Smagt, & Lorincz, 2017). They are also used in computer security applications such as malware detection.…”

Section: Adversarial Attacksmentioning

confidence: 99%

Machine learning in cybersecurity: A review

Handa

Sharma

Shukla

2019

WIREs Data Min & Knowl

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Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat analysis, anomaly based intrusion detection of prevalent attacks on critical infrastructures, and many others. Due to the ineffectiveness of signature‐based methods in detecting zero day attacks or even slight variants of known attacks, machine learning‐based detection is being used by researchers in many cybersecurity products. In this review, we discuss several areas of cybersecurity where machine learning is used as a tool. We also provide a few glimpses of adversarial attacks on machine learning algorithms to manipulate training and test data of classifiers, to render such tools ineffective. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Classification Application Areas > Data Mining Software Tools

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“…They are also fast because they obey weak consistency constraint instead of strong consistency constraint to trade consistency for processing speed. The data stored in the database are useful not only for the product whole lifecycle service, but also for modern data analysis schemes, like the big data analysis, 29 machine learning, 30 and deep learning 31 schemes, to perform prognostics and health management (PHM) 32 of machines, to optimize the production process, 30 to predict future demands, to improve services, and/or to save energy in production. 33 Since the cloud plays an important role in our CPS implementation, we now describe cloud implementation considerations.…”

Section: Focus On Both Vertical Integration and Horizontal Integrationmentioning

confidence: 99%

An improved cyber-physical systems architecture for Industry 4.0 smart factories

Jiang

2018

Advances in Mechanical Engineering

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The cyber-physical system is the core concept of Industry 4.0 for building smart factories. We can rely on the ISA-95 architecture or the 5C architecture to build the cyber-physical system for smart factories. However, both architectures emphasize more on vertical integration and less on horizontal integration. This article proposes the 8C architecture by adding 3C facets into the 5C architecture. The 3C facets are coalition, customer, and content. The proposed 8C architecture is a helpful guideline to build the cyber-physical system for smart factories. We show an example of designing and developing, on the basis of the proposed 8C architecture, a smart factory cyber-physical system, including an Industrial Internet of Things gateway and a smart factory data center running in the cloud environment.

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Overview of the CPS for Smart Factories Project: Deep Learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces

Cited by 32 publications

References 23 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Machine learning in cybersecurity: A review

An improved cyber-physical systems architecture for Industry 4.0 smart factories

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