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Prognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety, reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step. The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enables us to construct a library of health indicators (HI) from run-tofailure data. These HI's are then used online to estimate the RUL of components at an early stage of life, by comparing their HI's to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches.

In this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD). Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it's validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets.

In the Prognostics and Health Management (PHM) domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition, fusion, diagnostics, prognostics and decision are involved in this domain. This paper presents an approach to estimate the Remaining Useful Life of equipment based on shapelet extraction. This approach makes use, in an offline step, of a history of run-to-failure data to extract discriminative rul-shapelets, i.e. patterns that are correlated with the RUL of the considered equipment. A library of rul-shapelets is hence extracted at this step. Then, in an online step, these rul-shapelets are compared to testing units and the ones that match these units are used to estimate their RULs. Therefore, the RUL estimation of a testing unit is based on patterns that have been selected for their high correlation with the RUL. This approach is hence different from classical similaritybased approaches that attempt to match complete testing units (or only late instants of testing units) with training ones to estimate the RUL. The performance of our approach is assessed with a case study on the remaining useful life estimation of turbofan engines and performance is compared with other similarity-based approaches.

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