Estimation of Remaining Useful Life(RUL) of BLDC Motor using Machine Learning Approaches
R. Dhaya Sree, S. Jayanthy, E. Essaki Vigneshwaran · 2022 7th International Conference on Communication and Electronics Systems (ICCES) · 2022
The remaining useful life (RUL) is the capability of the machine to function before a replacement or repair is required. Estimation of RUL is considered to be important in predictive maintenance because it helps in optimizing the operating efficiency and to avoid unplanned downtime. The proposed project is based on the detection and calculation of RUL life of BLDC motor. For calculating the remaining useful life of BLDC motor parameters like temperature, current and voltage are measured from the sensors connected to the ARM7LPC2148 board and the values are given as data sets to the machine learning algorithm. The obtained data sets are trained using Machine learning algorithms like K-Nearest Neighbor(KNN), Gaussian Naïve Bayes,Random forest and Support vector machine(SVM).The main idea of the proposed project is that it can be used in the estimation of RUL in real time and they are also independent of external factors like temperature etc., Experimental results indicate that Random Forest algorithm is found to be efficient in terms of metrices like accuracy, precision, F1 score and recall. And hence RUL is estimated from random forest algorithm and the accuracy of the algorithm is 88%.