@inproceedings{b4be416e387940648e15b37528acb17d,
title = "A Degradation Modeling Method Based on Gamma Process with Artificial Neural Network Utilizing Two Types of Testing Data",
abstract = "To make the reliability estimation more practical and more accuracy, we proposed a method leverages two types of testing data to build the degradation model and reliability estimation. The corresponding artificial neural network training and inferencing the degradation process parameters are described. To enhance the accuracy of the degradation model, which is trained using both degradation testing data and life testing data, we describe the degradation process using a Gamma distribution. The parameters of Gamma process are set follow Gaussian distribution to describe the induvial difference and random effect. The parameters of Gaussian distribution given by moment estimation based on the training results. The accuracy of our proposed method is validated through a case study. The results indicate that our method offers distinct advantages in modeling the degradation process and in reliability estimation.",
keywords = "Artificial neural network, Degradation modeling, Degradation testing data, Gamma process, Life testing data",
author = "Xiaochuan Duan and Shaoping Wang and Di Liu and Enrui Wang and Yaoxing Shang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2200-9\_7",
language = "英语",
isbn = "9789819621996",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "67--78",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 1",
address = "德国",
}