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学者姓名:李寒
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Abstract :
In this paper, a systematic review of aero-engine defect detection methods is presented, encompassing the general procedure, traditional and intelligent detection algorithms, performance optimization, and future trends. The complete process and innovative theories of aero-engine visual defect detection are analyzed in this overview. Specifically, a five-level taxonomy is designed, with each level further subdivided to provide deeper insights, from data acquisition and task-oriented detection with nondestructive testing (NDT), to practical applications. By leveraging multiscale feature fusion-based detection, these methods achieve enhanced precision in identifying defects across varying scales and complexities. Moreover, in-depth discussions and outlooks on performance optimization and efficient deployment strategies are provided to promote advanced intelligent maintenance solutions for high-end equipment, which may encourage more multidisciplinary collaborations. Compared to other existing surveys, this work comprehensively outlines how computer vision (CV)-based methods can assist in aero-engine defect detection for intelligent decision-making, and a connection between NDT technology and CV-based inspection has been established, thereby drawing greater attention to the application of artificial intelligence to further enhance the development of industrial predictive maintenance.
Keyword :
aero-engine aero-engine computer vision computer vision defect detection defect detection industrial artificial intelligence industrial artificial intelligence multiscale feature fusion multiscale feature fusion
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GB/T 7714 | Wu, Peishu , Li, Han , Luo, Xin et al. From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (6) . |
MLA | Wu, Peishu et al. "From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines" . | MEASUREMENT SCIENCE AND TECHNOLOGY 36 . 6 (2025) . |
APA | Wu, Peishu , Li, Han , Luo, Xin , Hu, Liwei , Yang, Rui , Zeng, Nianyin . From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (6) . |
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In this paper, a novel metric learning framework with dual one-class units (MLF-DOU) is proposed to address the challenges of low accuracy and poor generalization ability associated with existing models in network intrusion detection. Specifically, the merits of one-class units are leveraged, enabling compact feature representations of both normal and attack traffic to be sufficiently extracted. This extraction is beneficial for mitigating the overfitting phenomenon. On this basis, a metric learning method is introduced to further enhance the recognition ability of the model for traffic in different categories. The inter-class distance is increased, and the fine-grained representations of intra-class similarity are strengthened. By these means, both the detection performance and the generalization ability of the proposed MLF-DOU are significantly improved. Extensive experimental results are presented to demonstrate the effectiveness of MLF-DOU across three datasets, showing its superiority over other state-of-the-art methods in achieving accurate intrusion detection. The effectiveness of key components within MLF-DOU is validated, contributing to robust feature learning for each class. Moreover, the adaptability of the proposed framework is proven, as it can be integrated with various network architectures, demonstrating promising potential for real-world deployments.
Keyword :
Generalization Generalization Metric learning Metric learning Network intrusion detection Network intrusion detection One-class classifier One-class classifier
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GB/T 7714 | Chen, Liang , Li, Han , Wu, Peishu et al. MLF-DOU: A metric learning framework with dual one-class units for network intrusion detection [J]. | NEUROCOMPUTING , 2025 , 649 . |
MLA | Chen, Liang et al. "MLF-DOU: A metric learning framework with dual one-class units for network intrusion detection" . | NEUROCOMPUTING 649 (2025) . |
APA | Chen, Liang , Li, Han , Wu, Peishu , Hu, Liwei , Chen, Tengpeng , Zeng, Nianyin . MLF-DOU: A metric learning framework with dual one-class units for network intrusion detection . | NEUROCOMPUTING , 2025 , 649 . |
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