An intelligent diagnosis framework for quality defects in aircraft assembly using weighted text embedding and a stacked sparse autoencoder
Papers|更新时间:2025-10-17
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An intelligent diagnosis framework for quality defects in aircraft assembly using weighted text embedding and a stacked sparse autoencoder
基于加权文本嵌入和堆叠稀疏自编码器的飞机装配质量缺陷智能诊断框架
“In the field of aircraft assembly quality defect diagnosis, a novel intelligent diagnosis framework is proposed. Expert researchers developed a stacked sparse AE network system, which provides solutions to accurately identify quality defects in long Chinese text data.”
Journal of Reliability Science and EngineeringIssue 3, Pages: 1-15(2025)
作者机构:
Department of Industrial Engineering & Management, and the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
作者简介:
[
"Zirong Wang received the BS degree in Quality and Reliability Engineering from Beihang University in 2022. In the Department of Industrial Engineering & Management, Shanghai Jiao Tong University, he received the MS degree in Industrial Engineering in 2025 and continued to pursue a PhD degree.",
"His current research interests include reliability modeling, battery health management."
]
[
"Zhen Chen received the BS degree in Quality and Reliability Engineering from Beihang University in 2014 and a PhD degree in Mechanical Engineering (Industrial Engineering) from Shanghai Jiao Tong University in 2019. He is now an assistant professor in the Department of Industrial Engineering & Management, Shanghai Jiao Tong University, China. He worked at the Department of Energy, Politecnico di Milano, Italy, as a postdoc.",
"His research interests include reliability modeling, condition monitoring and fault prognostics, battery health management."
]
[
"Zhaoxiang Chen received the BS degree in industrial engineering from the University of Electronic Science and Technology of China in 2017, the MS degree in control science and engineering from Beihang University in 2020, and a PhD degree in Mechanical Engineering (Industrial Engineering) from Shanghai Jiao Tong University in 2023.",
"He is currently a Postdoctoral with the Department of Industrial Engineering & Management, Shanghai Jiao Tong University. He is currently a Visiting Scholar with the Department of Industrial Systems Engineering and Management, National University of Singapore. His research interests include reliability assessment and maintenance decisions for manufacturing systems."
]
[
"Tangbin Xia received the PhD degree in Mechanical Engineering (Industrial Engineering) from Shanghai Jiao Tong University in 2014 and was a co-cultured PhD with the University of Michigan. Currently, he is an Associate Professor and serves as Deputy Director of the Department of Industrial Engineering at Shanghai Jiao Tong University.",
"He worked at ISyE, Georgia Institute of Technology as a Visiting Scholar. His major research interests are intelligent maintenance systems, prognostics & health management, and advanced manufacturing."
]
[
"Ershun Pan received BS and MS degrees in Mechanical design and manufacturing from Northeastern University, Shenyang, Liaoning, China, in 1997 and the PhD degree in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2000.",
"He is now a professor and the header in the Department of Industrial Engineering & Management, Shanghai Jiao Tong University, China. His current research interests include quality control, reliability engineering and maintenance policy, production system planning and design, and lean manufacturing technology."
]
Zirong Wang, Zhen Chen, Zhaoxiang Chen, et al. An intelligent diagnosis framework for quality defects in aircraft assembly using weighted text embedding and a stacked sparse autoencoder[J]. Journal of Reliability Science and Engineering, 2025, 1: 035401.
Zirong Wang, Zhen Chen, Zhaoxiang Chen, et al. An intelligent diagnosis framework for quality defects in aircraft assembly using weighted text embedding and a stacked sparse autoencoder[J]. Journal of Reliability Science and Engineering, 2025, 1: 035401. DOI: 10.1088/3050-2454/adfc76.