By Rafael Gouriveau, Kamal Medjaher, Noureddine Zerhouni
This publication addresses the stairs had to computer screen wellbeing and fitness review platforms and the anticipation in their disasters: selection and site of sensors, information acquisition and processing, health and wellbeing overview and prediction of the period of residual beneficial existence. The electronic revolution and mechatronics foreshadowed the appearance of the 4.0 the place apparatus has the facility to speak. The ubiquity of sensors (300,000 sensors within the new generations of plane) produces a flood of knowledge requiring us to offer aspiring to info and ends up in the necessity for effective processing and a proper interpretation. the method of traceability and capitalization of knowledge is a key aspect within the context of the evolution of the upkeep in the direction of predictive strategies. Read more...
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Extra info for From prognostics and health systems management to predictive maintenance 1: monitoring and prognostics
Actually, several points should be considered with utmost attention: – Structural and functioning decomposition of the system. – Interactions between its components. – Identiﬁcation and understanding of potential degradation mechanisms. – Choice of acquisition system. – Consideration of application constraints (size, inaccessibility to a measure). – etc. All this requires multidisciplinary skills and close collaboration with the manufacturer and/or the operator of the system who have precious information.
8] with r1 (t) = x(t). The global sifting process is repeated n times for the signal r1 in order to obtain the following components (IMFs), each with an increasing period. 9] When the signal rn becomes monotone, no IMF can be extracted anymore, and the decomposition process can be stopped. 10] j=1 where the last component rn , considered as a residual, is the average trend of the signal x(t). 9. , cn involve frequency bands, which are different one from another and depend on the signal x(t). 9.
19). Predictability is the ability of a sequence of data T S to be predicted by a modeling tool M , which produces forecasts on a temporal horizon H with a minimal performance level L. − ln( 12 ). 17] 56 From Prognostics and Health Systems Management to Predictive Maintenance 1 H M F ETHS/M = H 1 1 M i − T Si ei= . 18] where M F E indicates the mean forecast error between real data of T S and the values forecasted by the tool M . 19), and the more M F E is small, the more predictability is large (max=1).