Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference
Anomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/11 |
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| author | Qiang Guo Fenghe Li Hengwen Liu Jin Guo |
| author_facet | Qiang Guo Fenghe Li Hengwen Liu Jin Guo |
| author_sort | Qiang Guo |
| collection | DOAJ |
| description | Anomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tracing root causes of energy consumption in medium and heavy plate manufacturing, integrating process mechanisms, expert knowledge, and industrial big data. First, a two-stage anomaly detection method based on box plot analysis is developed to identify energy consumption irregularities. Next, a weighted Granger causality analysis method based on LSTM is introduced, which effectively captures the nonlinear and temporal relationships of process variables, enabling the identification of abnormal causal pathways. Finally, a root cause tracing algorithm using an Adam-based variational inference Bayesian neural network is proposed to pinpoint the underlying factors responsible for the anomalies. Experimental results validate the effectiveness of the proposed methods. |
| format | Article |
| id | doaj-art-9f9fb5532106461da4f10fe56b85f00d |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-9f9fb5532106461da4f10fe56b85f00d2025-01-24T13:17:28ZengMDPI AGAlgorithms1999-48932025-01-011811110.3390/a18010011Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational InferenceQiang Guo0Fenghe Li1Hengwen Liu2Jin Guo3National Engineering Research Center for Advanced Rolling Technology and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaNational Engineering Research Center for Advanced Rolling Technology and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaAnomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tracing root causes of energy consumption in medium and heavy plate manufacturing, integrating process mechanisms, expert knowledge, and industrial big data. First, a two-stage anomaly detection method based on box plot analysis is developed to identify energy consumption irregularities. Next, a weighted Granger causality analysis method based on LSTM is introduced, which effectively captures the nonlinear and temporal relationships of process variables, enabling the identification of abnormal causal pathways. Finally, a root cause tracing algorithm using an Adam-based variational inference Bayesian neural network is proposed to pinpoint the underlying factors responsible for the anomalies. Experimental results validate the effectiveness of the proposed methods.https://www.mdpi.com/1999-4893/18/1/11medium and heavy plateenergy consumptionanomaly detectionGranger causalityroot causeBayesian neural network |
| spellingShingle | Qiang Guo Fenghe Li Hengwen Liu Jin Guo Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference Algorithms medium and heavy plate energy consumption anomaly detection Granger causality root cause Bayesian neural network |
| title | Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference |
| title_full | Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference |
| title_fullStr | Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference |
| title_full_unstemmed | Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference |
| title_short | Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference |
| title_sort | anomaly detection and root cause analysis for energy consumption of medium and heavy plate a novel method based on bayesian neural network with adam variational inference |
| topic | medium and heavy plate energy consumption anomaly detection Granger causality root cause Bayesian neural network |
| url | https://www.mdpi.com/1999-4893/18/1/11 |
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