@inproceedings{4823ed6a9f754f31ae7855452de4c21b,
title = "CGAN-Based Missing Data Imputation for Industrial Data Fault Diagnosis",
abstract = "This paper proposes a missing data imputation method based on CGAN, enhanced with spectral normalization. To evaluate the effectiveness of the proposed method, two approaches are developed: a fault diagnosis model based on LSTM and CNN, and an FID score algorithm based on LSTM. These approaches transform the imputation results into more intuitive and measurable forms. Experimental results on the TE dataset demonstrate the strong performance of the proposed approach.",
keywords = "CGAN, CNN, fault diagnosis, FID score, LSTM, missing data imputation, spectral normalization",
author = "Zhengyang Chen and Yang Yu and Zhiming Yang and Gang Xiang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 16th IEEE Reliability and Prognostics and Health Management Conference, PHM-Xian 2025 ; Conference date: 10-10-2025 Through 12-10-2025",
year = "2025",
doi = "10.1109/PHM-Xian66756.2025.11427642",
language = "英语",
series = "2025 Global Reliability and Prognostics and Health Management Conference, PHM-Xian 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Huimin Wang and Steven Li",
booktitle = "2025 Global Reliability and Prognostics and Health Management Conference, PHM-Xian 2025",
address = "美国",
}