Ensemble learning and deep learning-based defect detection in power generation plants
One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses,...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-11-01
|
| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2023-0283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846171058584944640 |
|---|---|
| author | Atemkeng Marcellin Osanyindoro Victor Rockefeller Rockefeller Hamlomo Sisipho Mulongo Jecinta Ansah-Narh Theophilus Tchakounté Franklin Fadja Arnaud Nguembang |
| author_facet | Atemkeng Marcellin Osanyindoro Victor Rockefeller Rockefeller Hamlomo Sisipho Mulongo Jecinta Ansah-Narh Theophilus Tchakounté Franklin Fadja Arnaud Nguembang |
| author_sort | Atemkeng Marcellin |
| collection | DOAJ |
| description | One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs. |
| format | Article |
| id | doaj-art-a059a9ef84cf4c27b09e17e507eddd1f |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-a059a9ef84cf4c27b09e17e507eddd1f2024-11-11T08:36:35ZengDe GruyterJournal of Intelligent Systems2191-026X2024-11-0133140910.1515/jisys-2023-0283Ensemble learning and deep learning-based defect detection in power generation plantsAtemkeng Marcellin0Osanyindoro Victor1Rockefeller Rockefeller2Hamlomo Sisipho3Mulongo Jecinta4Ansah-Narh Theophilus5Tchakounté Franklin6Fadja Arnaud Nguembang7Department of Mathematics, Rhodes University, P.O. Box 94, 6139 Makhanda, South AfricaDepartment of Mathematical Sciences, African Institute for Mathematical Sciences, P.O. Box 608, Limbe, CameroonDepartment of Mathematical Sciences, African Institute for Mathematical Sciences, P.O. Box 608, Limbe, CameroonDepartment of Statistics, Rhodes University, P.O. Box 94, 6139 Makhanda, South AfricaDepartment of Mathematical Sciences, African Institute for Mathematical Sciences, P.O. Box 608, Limbe, CameroonGhana Space Science and Technology Institute, Ghana Atomic Energy Commission, P.O. Box 80, Accra, GhanaDepartment of SFTI, School of Chemical Engineering and Mineral Industries, University of Ngaoundéré, P.O. Box 454, Ngaoundéré, CameroonDipartimento di Matematica e Informatica, University of Ferrara, Via Saragat 1, I-44122 Ferrara, ItalyOne of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.https://doi.org/10.1515/jisys-2023-0283electric gridfuel consumptionautoencoderanomaly detectionpower generating plantsgansensemble learning |
| spellingShingle | Atemkeng Marcellin Osanyindoro Victor Rockefeller Rockefeller Hamlomo Sisipho Mulongo Jecinta Ansah-Narh Theophilus Tchakounté Franklin Fadja Arnaud Nguembang Ensemble learning and deep learning-based defect detection in power generation plants Journal of Intelligent Systems electric grid fuel consumption autoencoder anomaly detection power generating plants gans ensemble learning |
| title | Ensemble learning and deep learning-based defect detection in power generation plants |
| title_full | Ensemble learning and deep learning-based defect detection in power generation plants |
| title_fullStr | Ensemble learning and deep learning-based defect detection in power generation plants |
| title_full_unstemmed | Ensemble learning and deep learning-based defect detection in power generation plants |
| title_short | Ensemble learning and deep learning-based defect detection in power generation plants |
| title_sort | ensemble learning and deep learning based defect detection in power generation plants |
| topic | electric grid fuel consumption autoencoder anomaly detection power generating plants gans ensemble learning |
| url | https://doi.org/10.1515/jisys-2023-0283 |
| work_keys_str_mv | AT atemkengmarcellin ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT osanyindorovictor ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT rockefellerrockefeller ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT hamlomosisipho ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT mulongojecinta ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT ansahnarhtheophilus ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT tchakountefranklin ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants AT fadjaarnaudnguembang ensemblelearninganddeeplearningbaseddefectdetectioninpowergenerationplants |