Design Guidelines for Big Data Gathering in Industry 4.0 Environments

Bellavista, Paolo; Bosi, F.; Corradi, Antonio; Monti, Stefano; Patera, Lorenzo; Poli, Luca; Scotece, Domenico; Solimando, Michele

doi:10.1109/wowmom.2019.8793033

Cited by 5 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The definition of the physical architecture, i.e., the set technologies for the communication between layers, is not the main topic of the present paper. However, one can consider the architecture model presented in [80] and [81] as a possible guideline for the physical implementation of the proposed framework.…”

Section: The Frameworkmentioning

confidence: 99%

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

et al. 2021

View full text Add to dashboard Cite

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.

show abstract

Section: The Frameworkmentioning

confidence: 99%

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The presented architecture leverages a multi-layer architecture for several reasons. First, a multi-layer architecture is one of the key enablers for IT/OT convergence, as demonstrated in [9]. Second, it is possible to have different security policies at each layer according to the specifications.…”

Section: Proposed Architecturementioning

confidence: 99%

“…The IIoT is sometimes referred to as the integration of IT and the OT layers, where IT comprises the enterprise network and OT involves components interacting directly with the physical machines and extracting data from them. These two components have different security requirements which need to be considered to prevent compromising the IIoT infrastructure [9]. The OT layer involves components interacting directly with the physical machines and extracting data from them.…”

Section: Introductionmentioning

confidence: 99%

An SDN-Enabled Architecture for IT/OT Converged Networks: A Proposal and Qualitative Analysis under DDoS Attacks

2021

Self Cite

View full text Add to dashboard Cite

Real-time business practices require huge amounts of data directly from the production assets. This new thirst for accurate and timely data has forced the convergence of the traditionally business-focused information technology (IT) environment with the production-focused operational technology (OT). Recently, software-defined network (SDN) methodologies have benefitted OT networks with enhanced situational awareness, centralized configuration, deny-by-default forwarding rules, and increased performance. What makes SDNs so innovative is the separation between the control plane and the data plane, centralizing the command in the controllers. However, due to their young age, the use of SDNs in the industry context has not yet matured comprehensive SDN-based architectures for IT/OT networks, which are also resistant to security attacks such as denial-of-service ones, which may occur in SDN-based industrial IoT (IIoT) networks. One main motivation is that the lack of comprehensive SDN-based architectures for IT/OT networks making it difficult to effectively simulate, analyze, and identify proper detection and mitigation strategies for DoS attacks in IT/OT networks. No consolidated security solutions are available that provide DoS detection and mitigation strategies in IT/OT networks. Along this direction, this paper’s contributions are twofold. On the one hand, this paper proposes a convergent IT/OT SDN-based architecture applied in a real implementation of an IT/OT support infrastructure called SIRDAM4.0 within the context of the SBDIOI40 project. On the other hand, this paper proposes a qualitative analysis on how this architecture works under DoS attacks, focusing on what the specific problems and vulnerabilities are. In particular, we simulated several distributed denial-of-service (DDoS) attack scenarios within the context of the proposed architecture to show the minimum effort needed by the attacker to hack the network, and our obtained experimental results show how it is possible to compromise the network, thus considerably worsening the performance and, in general, the functioning of the network. Finally, we conclude our analysis with a brief description on the importance of employing machine learning approaches for attack detection and for mitigation techniques.

show abstract

SIRDAM4.0: A Support Infrastructure for Reliable Data Acquisition and Management in Industry 4.0

Corradi

Modica

Patera

et al. 2022

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

Design Guidelines for Big Data Gathering in Industry 4.0 Environments

Cited by 5 publications

References 7 publications

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

An SDN-Enabled Architecture for IT/OT Converged Networks: A Proposal and Qualitative Analysis under DDoS Attacks

SIRDAM4.0: A Support Infrastructure for Reliable Data Acquisition and Management in Industry 4.0

Contact Info

Product

Resources

About