IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

Abstract

With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid. Since the smart charging piles are generally deployed in complex environments and prone to failure, it is significant to perform efficient fault diagnosis and timely maintenance for them. One of the key problems to be solved is how to conduct fault prediction based on limited data collected through IoT in the early stage and develop reasonable preventive maintenance strategies. In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles. To demonstrate the advantage in prediction accuracy, the proposed fault prediction model is compared with the classic baseline models, such as LR, SVM, decision tree (DT), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (KNN), and backpropagation neural network (BPNN). Finally, based on the proposed fault prediction method, preventive maintenance based on a probability threshold with the minimum total expected cost is proposed. Simulation results show that the proposed maintenance strategy has a better performance in reducing the total maintenance cost compared with traditional periodic maintenance. This is valuable for the development of preventive maintenance strategies for repairable systems under early real-time monitoring data.

Topics & Concepts