A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network
In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/8/4/112 |
| _version_ | 1797537645121765376 |
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| author | Zhenzhong Xu Bang Chen Shenghan Zhou Wenbing Chang Xinpeng Ji Chaofan Wei Wenkui Hou |
| author_facet | Zhenzhong Xu Bang Chen Shenghan Zhou Wenbing Chang Xinpeng Ji Chaofan Wei Wenkui Hou |
| author_sort | Zhenzhong Xu |
| collection | DOAJ |
| description | In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fault diagnosis model is proposed in this paper based on Word to Vector (Word2vec) and prior-knowledge Convolutional Neural Network (CNN). The fault text first enters Word2vec to perform text feature extraction, and the extracted text feature vectors are then input into the proposed prior-knowledge CNN to train the fault classifier. The prior-knowledge CNN introduces expert fault knowledge through Cloud Similarity Measurement (CSM) to improve the performance of the fault classifier. Validation experiments on five-year maintenance log data of a civil aircraft were carried out to successfully verify the effectiveness of the proposed model. |
| first_indexed | 2024-03-10T12:19:22Z |
| format | Article |
| id | doaj.art-404d923725f14c19a0cd516273a63238 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T12:19:22Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-404d923725f14c19a0cd516273a632382023-11-21T15:35:55ZengMDPI AGAerospace2226-43102021-04-018411210.3390/aerospace8040112A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural NetworkZhenzhong Xu0Bang Chen1Shenghan Zhou2Wenbing Chang3Xinpeng Ji4Chaofan Wei5Wenkui Hou6Technology Development Department, Avicas Generic Technology Co., LTD, Yangzhou 225000, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaIn the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fault diagnosis model is proposed in this paper based on Word to Vector (Word2vec) and prior-knowledge Convolutional Neural Network (CNN). The fault text first enters Word2vec to perform text feature extraction, and the extracted text feature vectors are then input into the proposed prior-knowledge CNN to train the fault classifier. The prior-knowledge CNN introduces expert fault knowledge through Cloud Similarity Measurement (CSM) to improve the performance of the fault classifier. Validation experiments on five-year maintenance log data of a civil aircraft were carried out to successfully verify the effectiveness of the proposed model.https://www.mdpi.com/2226-4310/8/4/112text-drivenaircraft fault diagnosistext feature extractionconvolutional neural networkpriori knowledge |
| spellingShingle | Zhenzhong Xu Bang Chen Shenghan Zhou Wenbing Chang Xinpeng Ji Chaofan Wei Wenkui Hou A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network Aerospace text-driven aircraft fault diagnosis text feature extraction convolutional neural network priori knowledge |
| title | A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network |
| title_full | A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network |
| title_fullStr | A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network |
| title_full_unstemmed | A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network |
| title_short | A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network |
| title_sort | text driven aircraft fault diagnosis model based on a word2vec and priori knowledge convolutional neural network |
| topic | text-driven aircraft fault diagnosis text feature extraction convolutional neural network priori knowledge |
| url | https://www.mdpi.com/2226-4310/8/4/112 |
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