Proactive and dynamic event-driven disruption management in the manufacturing domain

Sejdovic, Suad; Kleiner, Natalja

doi:10.1109/indin.2016.7819371

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Internet of Things devices can be located by looking for behavioural characteristics. More than 66% of small firms have experienced a cyber attack in the last year, the majority of which are targeted, according to research on small business data used to understand network attacks [14] and the need to use machine learning to thwart them. Attacks on device security, including phishing, rose by 33%, and attempts to steal credentials, by 30% [17].…”

Section: Attack Severity Detectionmentioning

confidence: 99%

IoT Network Attack Severity Classification

Madhu,

Nayak,

Aerranagula

et al. 2023

E3S Web Conf.

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Lack of network security is a major roadblock for Internet of Things (IoT) implementations. New attacks have emerged in recent times, taking advantage of vulnerabilities in IoT gadgets. The sheer scale of the IoT will also make standard network attacks more potent. Machine learning has found a lot of use in traffic classification and intrusion detection. We present a methodology in this piece that can be used to spot fraudulent communications and determine the identity of IoT devices. To determine the origin of the generated traffic, the nature of the traffic, and the presence of network hazards, this framework collects features per network flow. To achieve this, it relocates the network’s brains to its periphery. In order to discover which of several Machine Learning algorithms is superior to random forest, a number of them are pitted against one another. Using these Machine Learning methods, attacks can be ranked in terms of their potential damage. After running the tests, it was determined that TABNET has the highest accuracy (94.62%) for categorizing the network severity (93.51%) and that CNN has the lowest accuracy (93.51%) of the two.

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Section: Attack Severity Detectionmentioning

confidence: 99%

IoT Network Attack Severity Classification

Madhu,

Nayak,

Aerranagula

et al. 2023

E3S Web Conf.

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“…To do so, a layered disruption framework is proposed to analyze several industrial scenarios, using both quantitative and qualitative approaches. In [34], the authors proposed a proactive disruption management tool which considered several disruption patterns, such as, machine breakdowns. This tool is based on an eicient initial scheduling scheme which aims at avoiding tasks rescheduling by absorbing the impacts of small disruptions.…”

Section: System Disruption Monitoring In Manufacturing Systemsmentioning

confidence: 99%

Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach

Brik

Messaadia²,

Sahnoun³

et al. 2022

ACM Trans. Cyber-Phys. Syst.

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Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things (IIoT) and Cyber-Physical-Production-Systems (CPPS), to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, etc. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g. a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real-time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this paper, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) which are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real-time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level which provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu-search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real-time.

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“…They are the most flexible integrators just when unexpected events need to be handled [9,10]. On the other hand, in this activity they need as a systematic and digitalized support as possible to record all relevant information, to find and configure appropriate resources for problem solving [3], and to escalating issues to higher levels of the management whenever shortage of resources or time requires it [11,12]. Automotive industry is pioneering in the development of such systems (focusing on network issues) [13], and we have found a disruption management system supporting the production of pressure diecasting cells [14].…”

Section: Introductionmentioning

confidence: 99%

An Escalation System for Handling and Analyzing Production Disturbances

Gödri

2024

Period. Polytech. Mech. Eng.

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In a manufacturing environment the effectiveness of internal problem solving is a key success factor in the service level performance offered to the customers. Broken internal processes need to be fixed immediately or at least in a very short time, in order to fulfill the committed delivery dates. There are several methodologies applied for internal problem solving used by different companies. This article presents a solution worked out for a plant operating in a high-mix low-volume (HMLV) production environment prone to both internal and external disruptions and disturbances. Principles, architecture and information flow in our digitized disruption handling – so-called escalation – system will be shortly discussed. Lessons learned in a six-year’s period of using the system will also be summarized. The recorded data confirm that with the introduction of the escalation system the capability of the plant to adapt to changing circumstances and disruptions greatly improved.

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Proactive and dynamic event-driven disruption management in the manufacturing domain

Cited by 9 publications

References 18 publications

IoT Network Attack Severity Classification

IoT Network Attack Severity Classification

Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach

An Escalation System for Handling and Analyzing Production Disturbances

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