Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT

Abstract

This study aims to detect electrical panel fires using the Internet of Things (IoT) framework and Machine Learning (ML) algorithms. Within the scope of the study, an experimental process was carried out using Arduino and Raspberry Pi platforms to collect essential data such as gas, temperature, and humidity. In this experimental work conducted on 3478 data points to detect electrical panel fires, Decision Tree (DT), Gaussian Naive Bayes (GNB), Gaussian Process Classifier (GPC), and Support Vector Machine (SVM) algorithms were used, and their performances were evaluated. For each algorithm, the best hyperparameters were selected using the 5 k-fold cross-validation method, and different models trained with these hyperparameters were examined. The findings indicate that the GPC algorithm has higher accuracy values than others, and its performance is consistent with a high potential for generalization. The GPC algorithm has stood out from the others with its high-performance values. For the GPC algorithm, the accuracy, precision, recall, F1 score, and Area Under the Curve (AUC) metrics values were 99.56%, 0.978, 0.989, 0.983, and 0.99, respectively. Additionally, Receiver Operating Characteristic (ROC) analysis has shown that GPC, DT, and SVM algorithms effectively distinguish positive and negative classes.