A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network

Xu, Zhenzhong; Chen, Bang; Zhou, Shenghan; Chang, Wenbing; Ji, Xinpeng; Wei, Chenxi; Hou, Wenkui

doi:10.3390/aerospace8040112

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…For different varied scenes, researchers may use varied feature extraction methods, which may be related to the different text features in each real scene: the fault text records in some scenes are better represented by keywords, whereas some scenes rely more on semantic analysis. Researchers usually choose appropriate methods for modeling by comparing the accuracy of different text feature extraction methods and machine learning methods [11,14,15]. Second, according to the extracted text features, they build an appropriate model for fault diagnosis, prediction, or other analysis.…”

Section: Text Feature Extractionmentioning

confidence: 99%

“…Li et al [17] processed the text data of typical power grid fault cases, established a corpus and transformed it into TF-IDF frequency matrix, clustered it using the K-means algorithm based on a Calinski-Harabaz index, and established the mapping table of fault information and solutions. Xu et al [14] introduced expert fault knowledge through a cloud-similarity measurement, improved the CNN model, and proposed a text driven aircraft fault diagnosis model based on a word-based and prior-knowledge CNN to diagnose aircraft faults through aircraft maintenance log data.…”

Section: Text Feature Extractionmentioning

confidence: 99%

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An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

et al. 2021

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At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

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Section: Text Feature Extractionmentioning

confidence: 99%

Section: Text Feature Extractionmentioning

confidence: 99%

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

et al. 2021

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“…Wang et al [31] proposed a bi-level (at syntax and semantic) feature extraction-based text mining technique for fault diagnosis to meet the challenges of high-dimensional data and imbalanced fault class distribution. Chen et al [32] proposed a prior-knowledge CNN that introduces expert fault knowledge through cloud similarity measurement (CSM) to improve the performance of a fault classifier.…”

Section: Data-driven Aircraft Fault Diagnosismentioning

confidence: 99%

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Zhou

Wei

et al. 2021

Aerospace

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Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.

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“…Another option to define similarity measures for cases is through the use of embeddings (Zou et al, 2020). For example, Xu et al (2021) used the word2vec model to obtain semantic information from fault data for fault classification. Cordeiro et al, (2019) proposed that the doc2vec model can be used to measure the semantic similarity of text in the oil and gas domain.…”

Section: Introductionmentioning

confidence: 99%

Finite-element analysis case retrieval based on an ontology semantic tree

Xu,

Cheng,

Xiao

et al. 2024

AIEDAM

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The widespread use of finite-element analysis (FEA) in industry has led to a large accumulation of cases. Leveraging past FEA cases can improve accuracy and efficiency in analyzing new complex tasks. However, current engineering case retrieval methods struggle to measure semantic similarity between FEA cases. Therefore, this article proposed a method for measuring the similarity of FEA cases based on ontology semantic trees. FEA tasks are used as indexes for FEA cases, and an FEA case ontology is constructed. By using named entity recognition technology, pivotal entities are extracted from FEA tasks, enabling the instantiation of the FEA case ontology and the creation of a structured representation for FEA cases. Then, a multitree algorithm is used to calculate the semantic similarity of FEA cases. Finally, the correctness of this method was confirmed through an FEA case retrieval experiment on a pressure vessel. The experimental results clearly showed that the approach outlined in this article aligns more closely with expert ratings, providing strong validation for its effectiveness.

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A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network

Cited by 14 publications

References 40 publications

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Finite-element analysis case retrieval based on an ontology semantic tree

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