作者
Jianjing Zhang, Peng Wang, Ruqiang Yan, Robert X Gao
发表日期
2018/7/1
期刊
Journal of manufacturing systems
卷号
48
页码范围
78-86
出版商
Elsevier
简介
Reliable tracking of performance degradation in dynamical systems such as manufacturing machines or aircraft engines and consequently, prediction of the remaining useful life (RUL) are one of the major challenges in realizing smart manufacturing. Traditional machine learning algorithms are often constrained in adapting to the complex and non-linear characteristics of manufacturing systems and processes. With the rapid development of modern computational hardware, Deep Learning has emerged as a promising computational technique for dynamical system prediction due to its enhanced capability to characterize the system complexity, overcoming the shortcomings of those traditional methods. In this paper, a new approach based on the Long Short-Term Memory (LSTM) network, an architecture that is specialized in discovering the underlying patterns embedded in time series, is proposed to track the …
学术搜索中的文章
J Zhang, P Wang, R Yan, RX Gao - Journal of manufacturing systems, 2018