@inproceedings{bc6786d4df8a4d0a8d42ac832ac838f7,
title = "Fault diagnosis based on multi-sensor information fusion using LSTM and D-S evidence theory",
abstract = "The aero-engine is so complex that make it very difficult to diagnosis faults. The fusion diagnosis is a hot research pot, and may give a new way to solve the above problems. LSTM neural network and D-S evidence theory methods are studied in detail, and a novel fault diagnosis method based on sensor information fusion is constructed. Taking C-Mapss turbofan engine simulation fault data as an example, the sensor data is classified, and the output result of LSTM neural network is used as input to construct a recognition framework of DS evidence fusion for fault diagnosis. The results show that this method can help avoiding false alarms to a certain extent, improving the accuracy of fault diagnosis.",
keywords = "component, D-S evidence theory, Fault diagnosis, LSTM, multi-sensor information fusion",
author = "Li Pengyu and Hou Wenkui and Yang Kun and Yan Qiuying and Ye Yong",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th International Conference on Information Science and Control Engineering, ICISCE 2020 ; Conference date: 18-12-2020 Through 20-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICISCE50968.2020.00133",
language = "英语",
series = "Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "617--621",
editor = "Shaozi Li and Ying Dai and Jianwei Ma and Yun Cheng",
booktitle = "Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",
address = "美国",
}