The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.
Wysowska, E., Wiewiórska, I., & Kicińska, A. (2021). The impact of different stages of water treatment process on the number of selected bacteria. Water Resources and Industry, 26, 100167.
Daily, J., & Peterson, J. (2017). Predictive maintenance: How big data analysis can improve maintenance. Supply Chain Integration Challenges in Commercial Aerospace: A Comprehensive Perspective on the Aviation Value Chain, 267-278.
Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in predictive maintenance–A review. CIRP Journal of Manufacturing Science and Technology, 40, 53-67.
Arafat, M. Y., Hossain, M. J., & Alam, M. M. (2024). Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects. Renewable and Sustainable Energy Reviews, 190, 114088.
Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., ... & Liotta, A. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67, 64-79.
Fathi, K., van de Venn, H. W., & Honegger, M. (2021). Predictive maintenance: an autoencoder anomaly-based approach for a 3 DoF delta robot. Sensors, 21(21), 6979.
Yu, H., Chen, C., Lu, N., & Wang, C. (2021). Deep auto-encoder and deep forest-assisted failure prognosis for dynamic predictive maintenance scheduling. Sensors, 21(24), 8373.
Revanur, V., Ayibiowu, A., Rahat, M., & Khoshkangini, R. (2020). Embeddings based parallel stacked autoencoder approach for dimensionality reduction and predictive maintenance of vehicles. In IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers 2 (pp. 127-141). Springer International Publishing.
Davari, N., Veloso, B., Ribeiro, R. P., Pereira, P. M., & Gama, J. (2021, October). Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE.
Santoso, B., Anggraeni, W., Pariaman, H., & Purnomo, M. H. (2022). RNN-Autoencoder Approach for Anomaly Detection in Power Plant Predictive Maintenance Systems. International Journal of Intelligent Engineering & Systems, 15(4).
S. Chang, B. Du and L. Zhang, "A Sparse Autoencoder Based Hyperspectral Anomaly Detection Algorithm Using Residual of Reconstruction Error," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 5488-5491, doi: 10.1109/IGARSS.2019.8898697.
A. R. Rezvanian, M. Imani and H. Ghassemian, "Patch-based Sparse and Convolutional Autoencoders for Anomaly Detection in Hyperspectral Images," 2020 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, 2020, pp. 1-5, doi: 10.1109/ICEE50131.2020.9261057.
M. Ganesh, A. Kumar and V. Pattabiraman, "Autoencoder Based Network Anomaly Detection," 2020 IEEE International Conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent (TEMSMET), Bengaluru, India, 2020, pp. 1-6, doi: 10.1109/TEMSMET51618.2020.9557464.
K. Wu, M. Cao, P. Wang and Z. Wang, "Convolutional One-Dimensional Variational Autoencoder Based Intrusion detection," 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), AB, Canada, 2021, pp. 488-493, doi: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00087.
Varatharaj Myilsamy et al., State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model. J. Electr. Eng. Technol. 19, 585–600 (2024). https://doi.org/10.1007/s42835-023-01564-2
S. Chouhan and B. Rudra, "Autoencoder-Based Anomaly Detection in ECG Image Time Series Data: A Comparative Evaluation of Three Different Architectures," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-6, doi: 10.1109/ICCCNT56998.2023.10306672.
K. Alrawashdeh and C. Purdy, "Fast Activation Function Approach for Deep Learning Based Online Anomaly Intrusion Detection," 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), Omaha, NE, USA, 2018, pp. 5-13, doi: 10.1109/BDS/HPSC/IDS18.2018.00016.
H. Park, J. Jeong, K. -W. Oh and H. Kim, "Autoencoder-Based Recommender System Exploiting Natural Noise Removal," IEEE Access, vol. 11, pp. 30609-30618, 2023, doi: 10.1109/ACCESS.2023.3262026.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Balaji V
Balaji V
Department of Computer Science and Engineering, GITAM University, Bengaluru, Karnataka, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Hussein Z. Almngoshi, Balaji V, Ramesh R, Arokia Jesu Prabhu L, Venubabu Rachapudi and Eswaramoorthy V, “Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection”, Journal of Machine and Computing, pp. 279-289, April 2024. doi: 10.53759/7669/jmc202404027.
