Read e-book online From prognostics and health systems management to predictive PDF

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 Read more...


This publication addresses the stairs had to computer screen healthiness evaluation platforms and the anticipation in their mess ups: selection and site of sensors, info acquisition and processing, wellbeing and fitness assessment Read more...

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Extra info for From prognostics and health systems management to predictive maintenance 1: monitoring and prognostics

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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).

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