Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network
The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated dail...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/23/3799 |
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| author | Hua Gong Wanning Xu Congang Chen Wenjuan Sun |
| author_facet | Hua Gong Wanning Xu Congang Chen Wenjuan Sun |
| author_sort | Hua Gong |
| collection | DOAJ |
| description | The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods. |
| format | Article |
| id | doaj-art-ae949fc6d2bb4319ab1a7bf792397118 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ae949fc6d2bb4319ab1a7bf7923971182024-12-13T16:27:48ZengMDPI AGMathematics2227-73902024-11-011223379910.3390/math12233799Production Quality Evaluation of Electronic Control Modules Based on Deep Belief NetworkHua Gong0Wanning Xu1Congang Chen2Wenjuan Sun3School of Science, Shenyang Ligong University, Shenyang 110159, ChinaLiaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang 110159, ChinaFaculty of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Zhuhai 519087, ChinaSchool of Science, Shenyang Ligong University, Shenyang 110159, ChinaThe electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods.https://www.mdpi.com/2227-7390/12/23/3799production qualitydeep belief networkmulti-mutation differential evolution algorithmquality evaluation |
| spellingShingle | Hua Gong Wanning Xu Congang Chen Wenjuan Sun Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network Mathematics production quality deep belief network multi-mutation differential evolution algorithm quality evaluation |
| title | Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network |
| title_full | Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network |
| title_fullStr | Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network |
| title_full_unstemmed | Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network |
| title_short | Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network |
| title_sort | production quality evaluation of electronic control modules based on deep belief network |
| topic | production quality deep belief network multi-mutation differential evolution algorithm quality evaluation |
| url | https://www.mdpi.com/2227-7390/12/23/3799 |
| work_keys_str_mv | AT huagong productionqualityevaluationofelectroniccontrolmodulesbasedondeepbeliefnetwork AT wanningxu productionqualityevaluationofelectroniccontrolmodulesbasedondeepbeliefnetwork AT congangchen productionqualityevaluationofelectroniccontrolmodulesbasedondeepbeliefnetwork AT wenjuansun productionqualityevaluationofelectroniccontrolmodulesbasedondeepbeliefnetwork |