This paper examines a CBM+ capability for medium power diesel generators by showing the results of an analysis of data that was collected from sensors that were placed on various components on a 30 kW generator; the paper focuses on fuel flow sensors that are sampled at a high rate. Condition Based Maintenance Plus (CBM+) contributes to optimal supply chain management of parts on a system platform by providing a prognosis of their remaining useful life (RUL). A data stream from sensors that are placed on a system to measure failure characteristics of its critical components is analyzed to develop a prognosis of system health. To that end, this paper demonstrates that detailed measurement at high sampling rates of physical effects of a component produces a predictive and reductive data signature of the performance of the component. Signatures evolve in time and a dynamical metric is proposed that evolves to produce a prognostic assessment of the condition of a component. Thus, prognostic assessments are done not only from time-series data but also the time-varying spectral analysis of the data. This means that a prognostic result is reached more quickly by using dynamic data signatures than it is from a time-series trend analysis.
The use of data signatures in Condition Based Maintenance Plus
01.03.2014
1497443 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
New Technology for Implementing Condition Based Maintenance Plus
British Library Conference Proceedings | 2005
|Online Contents | 2011
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