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 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.