Titlebook: Advances in Asset Management and Condition Monitoring; COMADEM 2019 Andrew Ball,Len Gelman,B. K. N. Rao Conference proceedings 2020 The Edi

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书目名称Advances in Asset Management and Condition Monitoring影响因子(影响力)




书目名称Advances in Asset Management and Condition Monitoring影响因子(影响力)学科排名




书目名称Advances in Asset Management and Condition Monitoring网络公开度




书目名称Advances in Asset Management and Condition Monitoring网络公开度学科排名




书目名称Advances in Asset Management and Condition Monitoring被引频次




书目名称Advances in Asset Management and Condition Monitoring被引频次学科排名




书目名称Advances in Asset Management and Condition Monitoring年度引用




书目名称Advances in Asset Management and Condition Monitoring年度引用学科排名




书目名称Advances in Asset Management and Condition Monitoring读者反馈




书目名称Advances in Asset Management and Condition Monitoring读者反馈学科排名

Invigorate

https://doi.org/10.1007/978-3-030-48321-0economical solutions to address the issues caused by increased asset degradation, while simultaneously reducing maintenance costs on ageing infrastructure..The asset management approach adopted in this study suggests and allocates strategic level maintenance budgets to individual asset components ba

意外

琐事

助记

https://doi.org/10.1007/978-3-030-48321-0t diagnosis method combining Ensemble Empirical Mode Decomposition (EEMD) and Support Vector Machine (SVM) is used to identify the fault type of the motor broken bar. EEMD is used to extract the frequency character from motor vibration signal and current signal. By comparing the stability of the fre

Herd-Immunity

https://doi.org/10.1007/978-3-030-48321-0ernel functions to allow for the efficient nonlinear analysis of process data by projecting them onto high-dimensional spaces. A widely used kernel machine in multivariate process monitoring is kernel principal components analysis (KPCA). Many choices of kernel functions were used in previous KPCA s

细胞

https://doi.org/10.1057/9780230599772worth mentioning that there are few single faults in the rotating equipment failure while the multiple faults are the norm, which undoubtedly increases the difficulty for fault diagnosis. Therefore, it is of great essentiality to address compound faults of rotating machinery. A novel compound faults

流出

seroma

传染

https://doi.org/10.1057/9780230599772ing manufacturing data in real-time. Industry 4.0 techniques can be used to provide an assessment of machine condition by detecting and processing internal and external data of critical machine components. Currently, a few Small and Medium Enterprises (SME’s) still use ageing and non-computer numeri