Yang, Jing and Tian, Ke and Zhao, Huayu and Feng, Zheng and Bourouis, Sami and Dhahbi, Sami and Khan, Abdullah Ayub and Berrima, Mouhebeddine and Por, Lip Yee (2025) Wastewater treatment monitoring: Fault detection in sensors using transductive learning and improved reinforcement learning. Expert Systems with Applications, 264. p. 125805. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2024.125805.
Full text not available from this repository.Abstract
Wastewater treatment plants (WWTPs) increasingly utilize sensors to optimize operations and ensure treated water quality. These sensors' rich datasets are well-suited for automated monitoring and fault detection. This study introduces a deep learning method for fault detection in sensors designed to tackle significant challenges, including a class imbalance in datasets where normal operational data significantly outnumber anomalies and sensitivity to hyperparameters. We employ a novel spatial attention-based transductive long short-term memory (TLSTM) network designed to detect subtle temporal variations in time-series data, facilitating the binary classification of faults in key processes like oxidation and nitrification. To address the challenge of data imbalance prevalent in WWTP monitoring, our model integrates the off-policy proximal policy optimization (Off-Policy PPO) framework. This adaptation enhances the traditional PPO algorithm for off-policy learning environments, improving data utilization and algorithm stability. In this system, data points are treated as a sequence of decisions, with the neural network functioning as an intelligent agent. The Off-Policy PPO approach employs a reward mechanism that prioritizes the correct prediction of minority-class instances over majority-class ones by assigning higher rewards. Moreover, the model incorporates the differential evolution (DE) algorithm for autonomous hyperparameter optimization, thereby minimizing reliance on manual tuning. Our rigorous testing on the Valdobbiadene dataset shows that our approach outperforms existing methods. Additionally, we apply transfer learning (TL) to the BSM1 dataset to further validate the model's effectiveness. Achieving F-measures of 87.24% on the Valdobbiadene dataset and 82.48% on the BSM1 dataset demonstrates the model's capability to promptly identify faults, significantly enhancing the reliability and efficiency of WWTP monitoring systems.
| Item Type: | Article |
|---|---|
| Funders: | Taif University (TU-DSPP-2024-60) |
| Uncontrolled Keywords: | Wastewater plant treatment; Fault detection; Imbalanced learning; Differential evolution; Reinforcement learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Mar 2025 08:25 |
| Last Modified: | 12 Mar 2025 08:25 |
| URI: | http://eprints.um.edu.my/id/eprint/47283 |
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