Towards Enhanced Prognostics with Advanced Data-Driven Modelling
作者： Andy R Mills , Robert F Harrison , Martha Arbayani Zaidan
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摘要: A considerable amount of prognostics research has been conducted to improve the remaining useful life prediction engineering assets. Advantages such as lowering sustainment costs and improving maintenance decision making, are significant motivations enhance the prognostics capability. Sensor selection, data pre-processing, knowledge elicitation the mathematical techniques some elements required capability. This paper takes a broad view explores available from variety application disciplines. dataflow diagram illustrates complete process discusses impact improvements in each step performance. The mathematical approach is crucial issue. Exploring cross-disciplinary prognostic approaches helpful extract different domains fuse strengths discipline. A case study fatigue induced crack-growth using Bayesian used illustrate that data-driven can deliver benefits industry.
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