A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/18/3454 |
| _version_ | 1798024332668043264 |
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| author | Adolfo Crespo Márquez Antonio de la Fuente Carmona Sara Antomarioni |
| author_facet | Adolfo Crespo Márquez Antonio de la Fuente Carmona Sara Antomarioni |
| author_sort | Adolfo Crespo Márquez |
| collection | DOAJ |
| description | In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions. |
| first_indexed | 2024-04-11T18:00:32Z |
| format | Article |
| id | doaj.art-148ff932aeea40c3be39d7b1c56ac44f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:00:32Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-148ff932aeea40c3be39d7b1c56ac44f2022-12-22T04:10:29ZengMDPI AGEnergies1996-10732019-09-011218345410.3390/en12183454en12183454A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy EfficiencyAdolfo Crespo Márquez0Antonio de la Fuente Carmona1Sara Antomarioni2Department of Industrial Management, Universidad de Sevilla, 41092 Seville, SpainDepartment of Industrial Management, Universidad de Sevilla, 41092 Seville, SpainDepartment of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, ItalyIn this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.https://www.mdpi.com/1996-1073/12/18/3454asset managementmaintenance managementdata miningartificial intelligenceenergy efficiency |
| spellingShingle | Adolfo Crespo Márquez Antonio de la Fuente Carmona Sara Antomarioni A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency Energies asset management maintenance management data mining artificial intelligence energy efficiency |
| title | A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency |
| title_full | A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency |
| title_fullStr | A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency |
| title_full_unstemmed | A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency |
| title_short | A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency |
| title_sort | process to implement an artificial neural network and association rules techniques to improve asset performance and energy efficiency |
| topic | asset management maintenance management data mining artificial intelligence energy efficiency |
| url | https://www.mdpi.com/1996-1073/12/18/3454 |
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