An Optimized Deep Learning Approach for Predicting the Electric Motor Temperature Using IOT Sensors
Mayapandi Mokkamayan, T. Suresh Padmanabhan · Electric Power Components and Systems · 2023
Based on that, the Internet of Things (IoT) is used in industrial applications for monitoring and controlling various sensor operations. In existing work, IoT-based monitoring and controlling operations for industries are proposed. But, in this, real-time monitoring of the data is performed to take necessary actions. This may fail at a fraction of a second when the device crossed its breakpoint or threshold value. Hence, in this, an optimized deep learning approach (Convolutional Neural Network) is proposed for monitoring and controlling the temperature in electrical motors. Here, the controlling is performed by predicting the temperature using a deep learning approach. This helps to improve the controlling operations in the IoT environment and protect the device from Malfunctioning. The proposed approach is tested on the Kaggle Sensor dataset for electrical motors. The optimal hyperparameters for the CNN are determined through the hybrid particle swarm and genetic algorithm by minimizing the cost function. The cost function is to reduce the RMSE rate. This method’s presentation is evaluated and the fidelity of root mean square merits. The whole process is implemented using MATLAB R2020a version under Windows 10 environment.