@inproceedings{0b90d42a29fb43169ab14dc308bcf5bc,
title = "GMDH-type neural network for remaining useful life estimation of equipment",
abstract = "The Group Method of Data Handing (GMDH)-type neural network algorithm is proposed to solve the problem of network structure design when using traditional neural network to predict Remaining Useful Life (RUL) of equipment. The Principal Component Analysis (PCA) algorithm is used to process the initial input data, which reduces the computational burden of the network. Using the Prediction Error Sum of Square (PESS) to select the hidden layer neurons, and the PCA method to limit the number of hidden neurons. Using the actual motor operating data to validate this algorithm, the results show that this method can adaptively construct equipment failure network model, avoid network structure selection problem, and has strong generalization ability.",
keywords = "GMDH, Machine Learning, Neural Network, Prognostics, Remaining Useful Life",
author = "Lin Zhao and Yipeng Wang and Yuan Liu and Yong Hao",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8029087",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "10844--10847",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "美国",
}