Research on Multi-Model Fusion for Multi-Indicator Collaborative Anomaly Prediction in IoT Devices

In the era of Industry 4.0, the widespread application of Internet of Things (IoT) technology enables us to monitor the operational status of production equipment through sensors and creates new requirements for equipment anomaly prediction and analysis. It can shift maintenance tasks from passive to proactive, reducing downtime and repair costs associated with equipment failures, and ensuring production safety and efficiency. However, the main challenge in industrial IoT applications lies in acquiring a sufficient amount of anomaly data. To address this issue, we propose a method that analyzes normal operating data of equipment to reveal trends in equipment status, combining multiple-model fusion prediction and multi-index coordinated decision-making. Firstly, a multi-model fusion strategy is adopted for prediction, integrating models such as XGBoost, LightGBM, and LSTM to enhance the accuracy of equipment attribute prediction. Secondly, through a multi-index coordinated approach, considering combinations of multiple indicators, an adaptive dynamic threshold rule is formulated based on the residuals between predicted and actual values to achieve early warning of equipment anomalies. Finally, we validate the effectiveness of this method using a mine ventilation fan as an example. The experiment demonstrated that this method can detect equipment anomalies one day earlier than traditional threshold alarm methods, achieving proactive equipment maintenance.