Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824003594 |
| _version_ | 1797215957709488128 |
|---|---|
| author | Muhammed Fatih Pekşen Ulaş Yurtsever Yılmaz Uyaroğlu |
| author_facet | Muhammed Fatih Pekşen Ulaş Yurtsever Yılmaz Uyaroğlu |
| author_sort | Muhammed Fatih Pekşen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T11:38:19Z |
| format | Article |
| id | doaj.art-66781e9ad42144fbb6b60d19fa953c5a |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-24T11:38:19Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-66781e9ad42144fbb6b60d19fa953c5a2024-04-10T04:28:43ZengElsevierAlexandria Engineering Journal1110-01682024-06-0196112123Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoTMuhammed Fatih Pekşen0Ulaş Yurtsever1Yılmaz Uyaroğlu2Vocational School of Adapazarı, Sakarya University, Sakarya 54050, Turkey; Corresponding author.Department of Computer Engineering, Sakarya University, Sakarya 54050, Turkey; Interdisciplinary Artificial Intelligence Lab., Research Development and Application Center (SARGEM), Sakarya University, Sakarya 54050, TurkeyDepartment of Electrical and Electronics Engineering, Sakarya University, Sakarya 54050, TurkeyThis 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.http://www.sciencedirect.com/science/article/pii/S1110016824003594Artificial intelligenceMachine learningIoTFirePredictive maintenance |
| spellingShingle | Muhammed Fatih Pekşen Ulaş Yurtsever Yılmaz Uyaroğlu Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT Alexandria Engineering Journal Artificial intelligence Machine learning IoT Fire Predictive maintenance |
| title | Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT |
| title_full | Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT |
| title_fullStr | Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT |
| title_full_unstemmed | Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT |
| title_short | Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT |
| title_sort | enhancing electrical panel anomaly detection for predictive maintenance with machine learning and iot |
| topic | Artificial intelligence Machine learning IoT Fire Predictive maintenance |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824003594 |
| work_keys_str_mv | AT muhammedfatihpeksen enhancingelectricalpanelanomalydetectionforpredictivemaintenancewithmachinelearningandiot AT ulasyurtsever enhancingelectricalpanelanomalydetectionforpredictivemaintenancewithmachinelearningandiot AT yılmazuyaroglu enhancingelectricalpanelanomalydetectionforpredictivemaintenancewithmachinelearningandiot |