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By Rafael Gouriveau, Kamal Medjaher, Noureddine Zerhouni

This ebook addresses the stairs had to visual display unit health and wellbeing overview structures and the anticipation in their disasters: selection and site of sensors, facts acquisition and processing, health and wellbeing evaluation and prediction of the length of residual precious lifestyles. The electronic revolution and mechatronics foreshadowed the arrival of the 4.0 the place apparatus has the facility to speak. The ubiquity of sensors (300,000 Read more...

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This ebook addresses the stairs had to visual display unit future health review platforms and the anticipation in their mess ups: selection and site of sensors, information acquisition and processing, well-being assessment Read more...

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Their usage supposes that degradation apparition leads to a significant variation of statistical parameters of the signal. This kind of feature is therefore frequently used in fault detection applications, but it is not of major interest for diagnostics and prognostics. If the signal (prior to fault) is not stationary, this kind of processing often produces false alarms. Noise presence can also distort the results and prevent early fault detection. 36 From Prognostics and Health Systems Management to Predictive Maintenance 1 Frequency analysis.

2. Feature selection by predictability Taking into account the previous remarks, the feature reduction/selection phase can be completed by considering only those features that are actually predictable by a prognostic tool. 20 resumes the steps necessary to that purpose. M , this approach aims at selecting the pairs Fi /Mj to be used for prognostics. In this spirit, feature extraction, selection, and prediction have to be carried out jointly. 3. Application and discussion Tests data: PHM challenge 2008.

N . 3) Calculate the covariance matrix S of the centered features matrix. The dimension of the covariance matrix is N × N . , N of the obtained covariance matrix. 5) Sort the obtained eigenvalues in decreasing order (from the biggest eigenvalue to the smallest one). 6) Keep p eigenvectors with the biggest eigenvalues, so that the desired variance p λi is greater or equal than i=1 N . In practice, the minimal retained variance is of 75%. λi i=1 7) Normalize the retained eigenvectors, so that their norm is equal to 1.

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