Towards Distributed IoT/Cloud based Fault Detection and Maintenance in Industrial Automation
Open accessAbstract
Industrial Internet of Things (IIoT) automation should be based on a framework that guarantees flexible and energy efficient monitoring and control, without the need for frequent human intervention. The ability to analyse and process machine faults in real time is vital, however it poses many technical difficulties and challenges, mainly for industrial application environments. In our paper, we propose a novel, energy efficient, IoT and Cloud based decentralised framework for real time machine condition monitoring (MCM) and fault prediction, where computational demanding tasks are distributed across fog nodes and decision fusion rules are set and controlled by the Cloud. In particular, data acquisition phase is done by sensors distributed across machines, feature extraction and health condition classification is done by fog nodes, after receiving data and instructions as processed by the Cloud node. Our framework is based on collaboration and information flow among IoT, Fog and Cloud layers. To this purpose, we formulate a global consensus cross layer optimisation problem, concerning industrial healthy status monitoring, and we solve it in a distributed manner by applying asynchronous altering direction method of multipliers (ADMM) algorithm.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many detection methods have been introduced in the literature to detect anomalies on data such as geometrical, statistical, and ML [132].
“Many detection methods have been introduced in the literature to detect anomalies on data such as, geometrical, statistical, and ML [132].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [132]
Sections 2.3 and 2.3.2 describe detecting outliers using a density-based classifier, a normal-distribution assumption, and Mahalanobis distance. The paper therefore supports the existence of an anomaly-detection method, but it does not survey the many methods in the literature or identify geometrical, statistical, and machine-learning methods as categories. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Fog Computing Paradigm: Scenarios and Security Issues
Stojmenovic, I., & Wen, S. (2014) · Annals of Computer Science and Information Systems · doi:10.15439/2014f503 · Cited by 1,037 · see citing works
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965 · see citing works
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2349359 · Cited by 763 · see citing works
Cognitive internet of things: A new paradigm beyond connection
Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2311513 · Cited by 550 · see citing works
Yan, J., Meng, Y., Lü, L., & Lin, L. (2017) · IEEE Access · doi:10.1109/access.2017.2765544 · Cited by 479 · see citing works
Asynchronous Distributed ADMM for Consensus Optimization
Zhang, R., & Kwok, J. T.-Y. (2014) · Cited by 322 · see citing works
Shirazi, S. N., Gouglidis, A., Farshad, A., & Hutchison, D. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2760478 · Cited by 284 · see citing works
One-Class Classification by Combining Density and Class Probability Estimation
Hempstalk, K., Frank, E., & Witten, I. H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87479-9_51 · Cited by 162 · see citing works
A Distributed Fault Detection System Based on IWSN for Machine Condition Monitoring
Neužil, J., Kreibich, O., & Šmíd, R. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2290432 · Cited by 79 · see citing works
Statistical modeling of signal amplitude fading of indoor radio propagation channels
Nikookar, H., & Hashemi, H. (2002) · IEEE International Conference on Universal Personal Communications (ICUPC) · doi:10.1109/icupc.1993.528353 · Cited by 52 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
“based anomaly detection is condition monitoring [132].”
A Novel Online Machine Learning Approach for Real-Time Condition Monitoring of Rotating Machines
Mostafavi, A., & Sadighi, A. (2021) · 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom54204.2021.9663495 · Cited by 20 · see citing works
- Authors
- Xenakis, A., Karageorgos, A., Lallas, E., Chis, A. E., & González-Vélez, H.
- Affiliations
- University of Thessaly (GR)
- National College of Ireland (IE)
- Corresponding author
- Xenakis, Apostolos
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 151
- Pages
- 683-690
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2945671985
- Cited by
- 43
- References
- 10
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 7 · 2022: 7 · 2021: 12 · 2020: 6 · 2019: 2
- FWCI
- 9.80
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- TRAP@NCI (National College of Ireland) (acceptedVersion)
- NORMA (submittedVersion)
- University of Thessaly Institutional Repository (University of Thessaly) (submittedVersion)
- Funders
- European Cooperation in Science and Technology
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- Industrial Internet of Things, machine condition monitoring, fault prediction, fog computing, cloud computing, distributed optimization, consensus optimization, alternating direction method of multipliers, energy efficiency, industrial automation, health status classification, decision fusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
