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IISE Transactions Volume 53, 2021 - Issue 3
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Quality & Reliability Engineering

A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics

Minhee KimDepartment of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, USAView further author information
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Kaibo LiuDepartment of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, USACorrespondence[email protected]
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Pages 326-340 | Received 26 Sep 2019, Accepted 26 Apr 2020, Published online: 24 Jun 2020

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Tangfan Xiahou, Yu Liu, Zhiguo Zeng & Muchen Wu. (2023) Remaining useful life prediction with imprecise observations: An interval particle filtering approach. IISE Transactions 55:11, pages 1075-1090.
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Hojin Cho, Kyeongbin Kim, Kihyuk Yoon, Jaewook Chun, Jaeyong Kim, Kyeongmin Lee, Junghye Lee & Chiehyeon Lim. (2023) MMP Net: A feedforward neural network model with sequential inputs for representing continuous multistage manufacturing processes without intermediate outputs. IISE Transactions 0:0, pages 1-12.
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Di Wang, Changyue Song & Xi Zhang. (2023) Multimodal regression and mode recognition via an integrated deep neural network. IISE Transactions 0:0, pages 1-17.
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Yucheng Dong, Siqi Wu, Xiaoping Shi, Yao Li & Francisco Chiclana. (2023) Clustering method with axiomatization to support failure mode and effect analysis. IISE Transactions 55:7, pages 657-671.
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Jianjun Shi. (2023) In-process quality improvement: Concepts, methodologies, and applications. IISE Transactions 55:1, pages 2-21.
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Minhee Kim, Jing-Ru C. Cheng & Kaibo Liu. (2021) An adaptive sensor selection framework for multisensor prognostics. Journal of Quality Technology 53:5, pages 566-585.
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Khanh T.P. Nguyen, Kamal Medjaher & Christian Gogu. (2022) Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems. Reliability Engineering & System Safety 222, pages 108383.
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Yuanhang Wang, Jun Wu, Yiwei Cheng, Ji Wang & Kui Hu. (2022) Memory-enhanced hybrid deep learning networks for remaining useful life prognostics of mechanical equipment. Measurement 187, pages 110354.
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Yuxin Wen, Md. Fashiar Rahman, Honglun Xu & Tzu-Liang Bill Tseng. (2022) Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement 187, pages 110276.
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Ahmad Kamal Mohd Nor, Srinivasa Rao Pedapati & Masdi Muhammad. 2022. International Conference on Artificial Intelligence for Smart Community. International Conference on Artificial Intelligence for Smart Community 755 774 .
Lei Xiao, Junxuan Tang, Xinghui Zhang, Eric Bechhoefer & Siyi Ding. (2021) Remaining useful life prediction based on intentional noise injection and feature reconstruction. Reliability Engineering & System Safety 215, pages 107871.
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Baiteng Ma, Xuegong Zhao & Lei Xiao. (2021) Remaining Useful Life Estimation Based On Feature Reconstruction And Variational Bayesian Inferences. Remaining Useful Life Estimation Based On Feature Reconstruction And Variational Bayesian Inferences.
Gang Zhang, Weige Liang, Bo She & Fuqing Tian. (2021) Rotating Machinery Remaining Useful Life Prediction Scheme Using Deep-Learning-Based Health Indicator and a New RVM. Shock and Vibration 2021, pages 1-14.
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Zhiyuan Xie, Shichang Du, Jun Lv, Yafei Deng & Shiyao Jia. (2020) A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction. Electronics 10:1, pages 39.
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