Ghinea Liliana Maria, Miron Mihaela, Barbu Marian
Department of Automatic Control and Electrical Engineering, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunărea de Jos University of Galați, 47 Domnească Str., 800008 Galați, Romania.
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunărea de Jos University of Galați, 47 Domnească Str., 800008 Galați, Romania.
Sensors (Basel). 2023 Sep 22;23(19):8022. doi: 10.3390/s23198022.
As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring the continuous effectiveness of plants. When these complex and dynamic systems are coupled with limited historical anomaly data or complex anomalies, it is crucial to have powerful tools capable of detecting subtle deviations from normal behavior to enable the early detection of equipment malfunctions. To address this challenge, in this study, we analyzed five semi-supervised machine learning techniques (SSLs) such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM), Multilayer Perceptron Autoencoder (MLP-AE), and Convolutional Autoencoder (Conv-AE) for detecting different anomalies (complete, concurrent, and complex) of the Dissolved Oxygen (DO) sensor and aeration valve in the WWTP. The best results are obtained in the case of Conv-AE algorithm, with an accuracy of 98.36 for complete faults, 97.81% for concurrent faults, and 98.64% for complex faults (a combination of incipient and concurrent faults). Additionally, we developed an anomaly detection system for the most effective semi-supervised technique, which can provide the detection of delay time and generate a fault alarm for each considered anomaly.
随着世界朝着数字化连接和可持续的未来发展，将半监督异常检测集成到废水处理过程（WWTPs）中有望成为保护水资源和确保工厂持续有效性的重要工具。当这些复杂且动态的系统与有限的历史异常数据或复杂异常相结合时，拥有能够检测出与正常行为细微偏差的强大工具以实现设备故障的早期检测至关重要。为应对这一挑战，在本研究中，我们分析了五种半监督机器学习技术（SSLs），如孤立森林（IF）、局部离群因子（LOF）、单类支持向量机（OCSVM）、多层感知器自编码器（MLP-AE）和卷积自编码器（Conv-AE），用于检测污水处理厂中溶解氧（DO）传感器和曝气阀的不同异常（完全、并发和复杂）。在Conv-AE算法的情况下获得了最佳结果，完全故障的准确率为98.36%，并发故障的准确率为97.81%，复杂故障（初始故障和并发故障的组合）的准确率为98.64%。此外，我们为最有效的半监督技术开发了一个异常检测系统，该系统可以提供延迟时间检测，并为每个考虑的异常生成故障警报。
