Artificial intelligence of things based approach for anomaly detection in rotating machines

Mian, Tauheed; Choudhary, Anurag; Fatima, Shahab; Panigrahi, B. K.

doi:10.1016/j.compeleceng.2023.108760

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…The efficiency of CyberEduPlatform in anomaly detection opens doors to more extensive implementations in other university networks ( Mian et al, 2023 ). The ability to quickly identify and respond to potential threats ensures a safer environment for the academic community.…”

Section: Resultsmentioning

confidence: 99%

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

Ortiz-Garcés,

Govea,

Sánchez-Viteri

et al. 2024

PeerJ Computer Science

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Cybersecurity has become a central concern in the contemporary digital era due to the exponential increase in cyber threats. These threats, ranging from simple malware to advanced persistent attacks, put individuals and organizations at risk. This study explores the potential of artificial intelligence to detect anomalies in network traffic in a university environment. The effectiveness of automatic detection of unconventional activities was evaluated through extensive simulations and advanced artificial intelligence models. In addition, the importance of cybersecurity awareness and education is highlighted, introducing CyberEduPlatform, a tool designed to improve users’ cyber awareness. The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats. This research highlights the need for an integrated approach to cybersecurity, combining advanced technological solutions with robust educational strategies.

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Section: Resultsmentioning

confidence: 99%

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

Ortiz-Garcés,

Govea,

Sánchez-Viteri

et al. 2024

PeerJ Computer Science

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“…Other examples in smart manufacturing facilities include systems with predictive maintenance strategies, forecasting potential equipment failures and optimizing operational efficiency [ 11 , 59 , 60 ]. Additionally, these systems excel in fault diagnosis by promptly identifying and diagnosing anomalies in real-time data streams [ 61 , 62 , 63 ].…”

Section: Iot-dt Frameworkmentioning

confidence: 99%

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Awouda,

Traini,

Bruno

et al. 2024

Sensors

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Digital twins are considered the next step in IoT-based cyber–physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

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“…In contrast to known methods for measuring the rotational parameters of electric motors, such as [12,13], the proposed technique provides an opportunity to adapt to changes in the characteristics of the electric motor by adjusting the neural network.…”

Section: Design Of An Experimental Prototype For Iden Tification Of A...mentioning

confidence: 99%

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

Kvasnikov,

Kvashuk,

Prygara

et al. 2023

EEJET

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The problem of the reliability of measurements of rotational parameters of electric motors was solved, which was focused on the development of an algorithm for evaluating measurements under conditions of additional noise. An analysis of methodological approaches and mathematical tools used to process and interpret the uncertainty of measurement results was carried out. Cases where they may not be effective due to high noise levels were considered. To detect anomalies in the signal, an algorithm for assessing the reliability of measurements using fuzzy logic was proposed. A structural diagram of the model for measuring the torque of an electric motor under the conditions of a noisy signal was developed, where transfer functions were used to model the angular velocity and torque parameters. A method for detecting anomalies in noisy signals is presented, which identifies the amplitude and time characteristics of spiking pulses. The method includes the application of a wide range of analytical tools for deep analysis of signals and is particularly effective for detecting anomalies that may be hidden in background noise. A prototype of a measuring bench was developed, which uses neural networks to detect anomalies when measuring the rotational parameters of electric motors, which made it possible to obtain a training sample using a sample electric motor and apply it to evaluate the parameters of another electric motor. In a practical aspect, the developed methods and technological solutions for improving the reliability of measurements of rotational parameters of electric motors could be used to make corrections in existing systems. In particular, they could be used in industry, electric transport, as well as in the aerospace and military sectors where the reliability of measuring systems is important

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Artificial intelligence of things based approach for anomaly detection in rotating machines

Cited by 14 publications

References 20 publications

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

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