Electrical Fault Diagnosis From Text Data: A Supervised Sentence Embedding Combined With Imbalanced Classification

Huge amounts of text data describing malfunction, defect, and safety hazard have been recorded in power maintenance sectors. Effectively mining such text data, and thus, classifying electrical fault types from text data bring the potential to considerably reduce the manpower in a variety of industri...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2024-03, Vol.71 (3), p.1-10
Main Authors: Jing, Xiao, Wu, Zhiang, Zhang, Lu, Li, Zhe, Mu, Dejun
Format: Article
Language:English
Subjects:
Ablation
Circuit faults
Classification
Deep learning
Electrical fault classification
Electrical faults
Embedding
Fault diagnosis
imbalanced multilabel classification
Industrial applications
Inspection
Knowledge representation
Maintenance engineering
power electronics
Predictive models
sentence embedding
Sentences
Task analysis
text classification
Training
Unstructured data
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Summary:Huge amounts of text data describing malfunction, defect, and safety hazard have been recorded in power maintenance sectors. Effectively mining such text data, and thus, classifying electrical fault types from text data bring the potential to considerably reduce the manpower in a variety of industrial applications such as power scheduling and periodic report generation. However, short sentences in verbal expressions, pervasive technical terms of electronics, and imbalanced fault type distribution put an enormous hindrance to fault diagnosis using unstructured data analytic approaches. It has conclusively been shown that deep learning is highly effective to learn representations for unstructured data. In this paper, we design and implement a deep-learning-based electrical fault identification framework, of which the core component is supervised Sentence Embedding combined with an Imbalanced Classification (SEIC) model. SEIC incorporates very little domain knowledge represented by class-related keywords into supervised sentence embedding. Meanwhile, both sentence embedding training and imbalanced multilabel classification training are guided by one unified objective. Experimental results on real-world dataset demonstrate that SEIC significantly improves the accuracy of electrical fault classification over existing deep models. Key factors affecting SEIC are carefully explored by an extensive ablation study.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2023.3269463