Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment
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In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyber-physic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.
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AR systems are faster than traditional approaches, users appreciate AR systems more in terms of likeability and usability, and AR seems to reduce physical workload, whereas the impact on mental workload depends on the interaction interface.
“Results of the analysis indicate that AR systems are faster than traditional approaches; users have greater appreciation for AR systems in terms of likeability and usability; and AR seems to reduce physical workload, whereas the impact on mental workload depends on the interaction interface [16].”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [16]
The Results and Discussions section evaluates machine-learning classifiers for induction-motor fault and cyberattack detection, not augmented-reality systems. Searches for AR speed, likeability, usability, and physical or mental workload found no passage addressing the cited claim. (LLM, conf=0.76)
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- Authors
- Tran, M.-Q., Elsisi, M., Mahmoud, K., Liu, M.-K., Lehtonen, M., & Darwish, M. M. F.
- Affiliations
- National Taiwan University of Science and Technology (TW)
- Aswan University (EG)
- Aalto University (FI)
- Benha University (EG)
- ORCID
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 115429-115441
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3195071730
- Cited by
- 158
- References
- 48
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 32 · 2023: 29 · 2022: 37 · 2021: 14
- FWCI
- 12.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Aaltodoc (Aalto University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
- Center for Cyber-physical System Innovation from the Featured Areas Research Center Program in the Agenda of the Higher Education Sprout Project, Taiwan
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- induction motor fault diagnosis, real-time fault diagnosis, Internet of Things, cyberattack detection, IoT architecture, Industry 4.0, cyber-physical systems, random forest, vibration-based fault detection
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