Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids
Abstract
The proliferation of Internet of Things (IoT) devices in the smart grid infrastructure has
enabled the generation of massive amounts of sensor data. This wealth of data presents
an opportunity to implement sophisticated data analytics techniques for predictive
maintenance in smart grids. Anomaly detection using machine learning algorithms has
emerged as a promising approach to identifying irregular patterns and deviations in
sensor data, leading to proactive maintenance strategies. This article explores the
application of machine learning techniques for anomaly detection in IoT sensor data to
enable predictive maintenance in smart grids. We delve into various machine learning
algorithms, including Isolation Forest, One-Class SVM, Autoencoders, and Random
Forest, assessing their capabilities in identifying anomalies in large-scale data streams.
The study also reviews the Performance Evaluation and Model Selection techniques for
Anomaly Detection in IoT Sensor Data, possible integration and deployment challenges,
and critique of the few selected studies. Explicitly, this scholarly inquiry questions the
profound significance of predictive maintenance within the context of Smart Grids. It
elucidates distinct categories of anomalies inherent within IoT Sensor Data.
Furthermore, the article expounds upon various classes of Machine Learning Algorithms
while also clarifying the criteria employed for their selection. Notably, the study probes
the potential hindrances that could emerge during the deployment and integration of
Machine Learning Techniques specifically aimed at Anomaly Detection in IoT Sensor
Data. In addition, the research sheds light on the aspects that might have been
inadvertently overlooked within the existing corpus of literature.
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References
Bibri, Simon Elias, and John Krogstie. "The emerging data–driven Smart City and its innovative applied
solutions for sustainability: The cases of London and Barcelona." Energy Informatics 3 (2020): 1-42.
Wankhede, Sonali B. "Anomaly detection using machine learning techniques." 2019 IEEE 5th International
Conference for Convergence in Technology (I2CT). IEEE, 2019.
Bose, Sumon Kumar, et al. "ADEPOS: Anomaly detection-based power saving for predictive maintenance using
edge computing." Proceedings of the 24th asia and south pacific design automation conference. 2019.
Makridis, Georgios, Dimosthenis Kyriazis, and Stathis Plitsos. "Predictive maintenance leveraging machine
learning for time-series forecasting in the maritime industry." 2020 IEEE 23rd international conference on
intelligent transportation systems (ITSC). IEEE, 2020.
Ghosh, Nimisha, et al. "Outlier detection in sensor data using machine learning techniques for IoT framework
and wireless sensor networks: A brief study." 2019 International Conference on Applied Machine Learning
(ICAML). IEEE, 2019.
Al-amri, Redhwan, et al. "A review of machine learning and deep learning techniques for anomaly detection in
IoT data." Applied Sciences 11.12 (2021): 5320.
Omol, E., & Ondiek, C. “Technological Innovations Utilization Framework: The Complementary Powers of
UTAUT, HOT–Fit Framework and; DeLone and McLean IS Model.” International Journal of Scientific and
Research Publications (IJSRP), 11(9), 146-151. DOI: 10.29322/IJSRP.11.09. 2021.p11720
http://dx.doi.org/10.29322/IJSRP.11.09.2021.p11720
Mishra, Sakshi, Andrew Glaws, and Praveen Palanisamy. "Predictive Analytics in Future Power Systems: A
Panorama and State-Of-The-Art of Deep Learning Applications." Optimization, Learning, and Control for
Interdependent Complex Networks (2020): 147-182.
Stimmel, Carol L. Big data analytics strategies for the smart grid. CRC press, 2014.
Omol, E., Mburu, L., & Abuonji, P . “Digital Maturity Action Fields for SMEs in Developing Economies.”
Journal of Environmental Science, Computer Science, and Engineering & Technology, 12(3), 2023
https://doi.org/10.24214/jecet.B.12.3.10114.
Gałka, Łukasz, Paweł Karczmarek, and Mikhail Tokovarov. "Isolation Forest based on minimal spanning
tree." IEEE Access 10 (2022): 74175-74186.
Razzak, Imran, et al. "Randomized nonlinear one-class support vector machines with bounded loss function to
detect of outliers for large scale IoT data." Future Generation Computer Systems 112 (2020): 715-723.
Bank, Dor, Noam Koenigstein, and Raja Giryes. "Autoencoders." arXiv preprint arXiv:2003.05991 (2020).
Sheykhmousa, Mohammadreza, et al. "Support vector machine versus random forest for remote sensing image
classification: A meta-analysis and systematic review." IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing 13 (2020): 6308-6325.
Omol, Edwin, Silvance Abeka, and Fred Wauyo. "E-Proctored Model: Electronic Solution Architect for Exam
Dereliction in Kenya." (2017).
Omol, Edwin J., et al. "Mobile Money Payment Acceptance Model in Enterprise Management: A Case Study of
MSEs in Kisumu City, Kenya." Mara Research Journal of Information Science & Technology Vol. 1 (2016):
-12.
Fenza, Giuseppe, Mariacristina Gallo, and Vincenzo Loia. "Drift-aware methodology for anomaly detection in
smart grid." IEEE Access 7 (2019): 9645-9657.
Gao, Lu, Pan Lu, and Yihao Ren. "A deep learning approach for imbalanced crash data in predicting highwayrail grade crossings accidents." Reliability Engineering & System Safety 216 (2021): 108019.
Baldo, Nicola, Evangelos Manthos, and Matteo Miani. "Stiffness modulus and marshall parameters of hot mix
asphalts: Laboratory data modeling by artificial neural networks characterized by cross-validation." Applied
Sciences 9.17 (2019): 3502.
Mantovani, Rafael G., et al. "Effectiveness of random search in SVM hyper-parameter tuning." 2015
international joint conference on neural networks (IJCNN). Ieee, 2015.
Stacke, Karin, et al. "Measuring domain shift for deep learning in histopathology." IEEE journal of biomedical
and health informatics 25.2 (2020): 325-336.
International Journal of Science, Technology & Management ISSN: 2722 - 4015
Oluwasegun, Adebena, and Jae-Cheon Jung. "A multivariate Gaussian mixture model for anomaly detection in
transient current signature of control element drive mechanism." Nuclear Engineering and Design 402 (2023):
Smith, J. A., Johnson, M. R., & Brown, L. K. (2019). Anomaly Detection in Smart Grid Sensor Data Using
Machine Learning for Predictive Maintenance. International Journal of Smart Grid and Clean Energy
Martinez, S. M., Gonzalez, R. F., & Ramirez, E. H. (2020). Machine Learning-Based Anomaly Detection for
Predictive Maintenance in Smart Grids. IEEE International Conference on Smart Grid Communications.
Kim, H., Lee, S., & Park, J. (2021). IoT-Driven Anomaly Detection for Predictive Maintenance in Smart Grids
Using Deep Learning. Sensors.
Chen, Y., Liu, Y., & Zhang, Z. (2021). Machine Learning Techniques for Anomaly Detection and Predictive
Maintenance in the Smart Grid. Energies.
Wang, Q., Zhang, J., & Zhou, J. (2021). A Review of Anomaly Detection Techniques for Predictive
Maintenance in Smart Grids. IEEE Access.
Omol, Edwin, Silvance Abeka, and Fred Wauyo. "Factors Influencing Acceptance of Mobile money
Applications in Enterprise Management: A Case Study of Micro and Small Enterprise Owners in Kisumu
Central Business District, Kenya." IJARCCE 6 (2017): 208-219.
Omol, Edwin Juma. "Organizational digital transformation: from evolution to future trends." Digital
Transformation and Society (2023).
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