Convolutional Neural Network Enactment Layer based Ambient Temperature Prediction of Electric Motor

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M. Shyamala Devi, et. al.

Abstract

As we know that Plan temperature evaluations apply to the most smoking spot inside the motor’s windings, not how much of that warm is exchanged to the motor’s surface. The warm exchange will change incredibly from engine to engine based on outline measure and mass, whether the outline is smooth or ribbed, whether open or completely encased, and other cooling variables. Indeed the productivity of the engine may have small impact on the surface temperature. An exact torque assess leads to more exact and satisfactory control of the engine, diminishing control misfortunes and in the long run heat build-up. In this venture, the machine learning strategies were utilized for the expectation of surrounding temperature of Electric engine. The forecast of ambient temperature of Electric engine are accomplished in four ways. With this context, we have utilized Electric motor temperature dataset extracted from UCI Machine Learning repository for predicting the ambient temperature prediction.  The forecasting of ambient temperature are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, empirical feature examination is done and the relation of motor speed and ambient temperature of the motor is visualized. Thirdly, the fresh data set is fitted to all the regressors and the execution is dismembered before and after scaling. Fourth, the raw data set is subjected to Convolutional neural network Conv1D with various activation layers like Relu, Sigmoid, softmax, Softplus, Softsign, Tanh, Selu, Elu and exponential layers. The performance is analyzed with EVS, MAE, MSE, RScore and Step loss of the Convolutional neural network. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Conv1D-Softsign activation layer tends to reach the RScore of 99.842 with the step loss of 0.0001155.

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