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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書目名稱Advances in Asset Management and Condition Monitoring影響因子(影響力)




書目名稱Advances in Asset Management and Condition Monitoring影響因子(影響力)學(xué)科排名




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書目名稱Advances in Asset Management and Condition Monitoring網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Advances in Asset Management and Condition Monitoring被引頻次學(xué)科排名




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書目名稱Advances in Asset Management and Condition Monitoring讀者反饋




書目名稱Advances in Asset Management and Condition Monitoring讀者反饋學(xué)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

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助記

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

細(xì)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

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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