Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

Ghinea, Liliana Maria; Miron, Mihaela; Barbu, Marian

doi:10.3390/s23198022

Cited by 4 publications

(2 citation statements)

References 43 publications

(49 reference statements)

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“…Shallow machine learning involves finding and learning patterns in large amounts of data. Wang et al [9] proposed an anomaly detection model for an integrated energy system that included electricity, gas, and heat using a support vector machine (SVM) [10], and they demonstrated the superior performance of the model to statistical models. Lee et al [11] proposed an anomaly detection model for aircraft using an SVM.…”

Section: Related Workmentioning

confidence: 99%

“…First, we obtain the output at the current time t using the hidden states of the encoder h i and decoder s t by calculating the attention score corresponding to the similarity of all h i and s t using Equation ( 9). e t is a scalar value consisting of the attention score, as expressed in Equation (10). The attention distribution is obtained by converting this scalar value into a probability distribution by applying the softmax function to e t , as given via Equation (11).…”

Section: Attention Mechanismmentioning

confidence: 99%

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CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

Chung,

Gu,

Yoo

2023

Sensors

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The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN–LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model.

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Section: Related Workmentioning

confidence: 99%

Section: Attention Mechanismmentioning

confidence: 99%

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

Chung,

Gu,

Yoo

2023

Sensors

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IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

Huang,

Li,

et al. 2024

Int J Comput Intell Syst

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Anomaly detection is a critical line of defense to ensure the network security of industrial cyber-physical systems. However, a significant issue in the anomaly detection is the insufficient labels of anomaly classes. With emergence of the new and unknown network attacks, accurately labeling these attacks can be a costly task. The issue of inadequate labeling may negatively impact the detection performance of many existing anomaly detection methods. To meet this gap, this paper proposes a semi-supervised collaborative learning paradigm called IDG-SemiAD, based on an immune detector generation algorithm. First, we design an immune detector generation algorithm based on a chaos map to generate abnormal samples from self-samples. Then, these abnormal samples are combined with self-samples and given specific labels to form a new training set. Finally, the LightGBM classifier is used for training and detection. Experiments on the widely used public dataset BATADAL show that the proposed IDG-SemiAD outperforms the classical v-detector method in terms of recall and f-score, with improvements of 8.2% and 8%, respectively, and outperforms deep learning-based anomaly detection methods, with a maximum improvements of up to 89.7% and 59.5% respectively.

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Enhancing anomaly detection: A comprehensive approach with MTBO feature selection and TVETBO Optimized Quad-LSTM classification

Reddy N V,

B．N

et al. 2024

Computers and Electrical Engineering

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Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

Cited by 4 publications

References 43 publications

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

Enhancing anomaly detection: A comprehensive approach with MTBO feature selection and TVETBO Optimized Quad-LSTM classification

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