Application of big data technology in electromechanical operation and maintenance intelligent platform
Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, thi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-08-01
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| Series: | Paladyn |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/pjbr-2022-0121 |
| _version_ | 1797418989353172992 |
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| author | Yang Wenjuan Chan Zhongbin Wang Yi Qi Fuli |
| author_facet | Yang Wenjuan Chan Zhongbin Wang Yi Qi Fuli |
| author_sort | Yang Wenjuan |
| collection | DOAJ |
| description | Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags. |
| first_indexed | 2024-03-09T06:42:02Z |
| format | Article |
| id | doaj.art-2249d51fda65480c915f8f61e93c51bc |
| institution | Directory Open Access Journal |
| issn | 2081-4836 |
| language | English |
| last_indexed | 2024-03-09T06:42:02Z |
| publishDate | 2023-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Paladyn |
| spelling | doaj.art-2249d51fda65480c915f8f61e93c51bc2023-12-03T10:46:53ZengDe GruyterPaladyn2081-48362023-08-01141p. 0121097810.1515/pjbr-2022-0121Application of big data technology in electromechanical operation and maintenance intelligent platformYang Wenjuan0Chan Zhongbin1Wang Yi2Qi Fuli3School of Information Engineering, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, ChinaSchool of Information Engineering, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, ChinaSchool of Information Engineering, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, ChinaSchool of Information Engineering, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, ChinaAiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.https://doi.org/10.1515/pjbr-2022-0121big data technologyelectromechanical operation and maintenance intelligent platformdata cleaningfault predictionsmall amount of labels |
| spellingShingle | Yang Wenjuan Chan Zhongbin Wang Yi Qi Fuli Application of big data technology in electromechanical operation and maintenance intelligent platform Paladyn big data technology electromechanical operation and maintenance intelligent platform data cleaning fault prediction small amount of labels |
| title | Application of big data technology in electromechanical operation and maintenance intelligent platform |
| title_full | Application of big data technology in electromechanical operation and maintenance intelligent platform |
| title_fullStr | Application of big data technology in electromechanical operation and maintenance intelligent platform |
| title_full_unstemmed | Application of big data technology in electromechanical operation and maintenance intelligent platform |
| title_short | Application of big data technology in electromechanical operation and maintenance intelligent platform |
| title_sort | application of big data technology in electromechanical operation and maintenance intelligent platform |
| topic | big data technology electromechanical operation and maintenance intelligent platform data cleaning fault prediction small amount of labels |
| url | https://doi.org/10.1515/pjbr-2022-0121 |
| work_keys_str_mv | AT yangwenjuan applicationofbigdatatechnologyinelectromechanicaloperationandmaintenanceintelligentplatform AT chanzhongbin applicationofbigdatatechnologyinelectromechanicaloperationandmaintenanceintelligentplatform AT wangyi applicationofbigdatatechnologyinelectromechanicaloperationandmaintenanceintelligentplatform AT qifuli applicationofbigdatatechnologyinelectromechanicaloperationandmaintenanceintelligentplatform |