Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

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Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

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作者：Yiqi Liu^a ; ^{aulyq@scut.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Hongjun Xiao^a ; Yongping Pan^b ; ^{biepany@nus.edu.sg" class="auth_mail" title="E-mail the corresponding author} ; Daoping Huang^a ; ^{audhuang@scut.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Qilin Wang^c ; ^{q.wang@awmc.uq.edu.au" class="auth_mail" title="E-mail the corresponding author}
关键词：Soft-sensors ; Fault diagnosis ; Fault prognosis ; Multi-step-ahead prediction ; Wastewater
刊名：Chemometrics and Intelligent Laboratory Systems
出版年：2016
出版时间：15 October 2016
年：2016
卷：157
期：Complete
页码：85-95
全文大小：2275 K

文摘

•: A GPR is used for multi-step prediction depending on the data trend.
•: Direct, Recursive and Direct-recursive methods are employed to generalize GPR model.
•: The uncertainty information generated from GPR facilitates fault pre-caution.
•: Multi-step prediction can support fault diagnosis and prognosis efficiently.

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