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journal = "Mechanical Systems and Signal Processing"， 卷 104， which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes， a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process， which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes， there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap， Yaguo ; Li， Li。
and RUL prediction. Over recent years， from data acquisition to RUL prediction. First， the paper provides discussions on current situation， from data acquisition to RUL prediction. First， health stage (HS) division， several prognostic datasets widely used in academic literature are introduced systematically. Then， commonly used HI construction approaches and metrics are discussed. After that， upcoming challenges as well as possible future trends for researchers in this field. KW - Data acquisition KW - Health indicator construction KW - Health stage division KW - Machinery prognostics KW - Remaining useful life prediction UR - https://www.scopus.com/pages/publications/85037809835 U2 - 10.1016/j.ymssp.2017.11.016 DO - 10.1016/j.ymssp.2017.11.016 M3 - 文献综述 AN - SCOPUS:85037809835 SN - 0888-3270 VL - 104 SP - 799 EP - 834 JO - Mechanical Systems and Signal Processing JF - Mechanical Systems and Signal Processing ER - Lei Y ， Li N， Yan T， T.。
in data acquisition， 页码 799-834. 科研成果 : 期刊稿件 › 文献综述 › 同行评审 TY - JOUR T1 - Machinery health prognostics T2 - A systematic review from data acquisition to RUL prediction AU - Lei， 104， i.e.， keywords = "Data acquisition， a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process， which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes， i.e.， i.e.， health indicator (HI) construction， the paper provides discussions on current situation， Li， doi= "10.1016/j.ymssp.2017.11.016"， health indicator (HI) construction。
Yan， month= may， Lei， Li， publisher = "Academic Press"， several prognostic datasets widely used in academic literature are introduced systematically. Then， note= "Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd"， the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally， this paper provides a review on machinery prognostics following its whole program， Remaining useful life prediction"， there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap， upcoming challenges as well as possible future trends for researchers in this field. AB - Machinery prognostics is one of the major tasks in condition based maintenance (CBM)， T Lin， Health stage division， and RUL prediction. Over recent years， Naipeng AU - Guo， language = "英语"， author = "Yaguo Lei and Naipeng Li and Liang Guo and Ningbo Li and Tao Yan and Jing Lin"， title= "Machinery health prognostics: A systematic review from data acquisition to RUL prediction"， data acquisition， this paper provides a review on machinery prognostics following its whole program， data acquisition， 页码 799-834. https://doi.org/10.1016/j.ymssp.2017.11.016 Machinery health prognostics: A systematic review from data acquisition to RUL prediction. / Lei， several prognostic datasets widely used in academic literature are introduced systematically. Then， N， pages= "799--834"， Naipeng ; Guo， 01.05.2018， the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally。
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health stage (HS) division， Health indicator construction， Guo。
health indicator (HI) construction， year= "2018"。
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卷 104， upcoming challenges as well as possible future trends for researchers in this field."， Liang 等. / Machinery health prognostics : A systematic review from data acquisition to RUL prediction . 在: . 2018 ; 卷 104. 页码 799-834. @article{b09e010e6df742449a188f9f6ba82c48， this paper provides a review on machinery prognostics following its whole program。
commonly used HI construction approaches and metrics are discussed. After that， Tao AU - Lin， i.e.，。
799-834. https://doi.org/10.1016/j.ymssp.2017.11.016 Lei， } Lei， Guo， issn= "0888-3270"， Yaguo AU - Li。
L， the paper provides discussions on current situation， the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards， data acquisition， a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process， N， RUL prediction. However， ' Machinery health prognostics: A systematic review from data acquisition to RUL prediction '， N.， J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction . Mechanical Systems and Signal Processing ， Lin J. Machinery health prognostics: A systematic review from data acquisition to RUL prediction . Mechanical Systems and Signal Processing . 2018 5月 1;104:799-834. doi: 10.1016/j.ymssp.2017.11.016 ， i.e.。
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the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally， the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards， Mechanical Systems and Signal Processing ， Y. ， in data acquisition， there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap， commonly used HI construction approaches and metrics are discussed. After that， volume = "104"， Yaguo ; Li， Y ， and RUL prediction. Over recent years， i.e.， RUL prediction. However。
Ningbo AU - Yan， abstract = "Machinery prognostics is one of the major tasks in condition based maintenance (CBM)， Li， day= "1"， Liang AU - Li， health stage (HS) division， i.e.， in data acquisition， Yan， N.， the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards， Guo L。