Quantification of feature goodness, called feature evaluation, is crucial in the identification of best features and achieving high accuracy in diagnostics and prognostics. Even though feature evaluation for diagnostics is a mature area, it is a developing research area for prognostics. The feature goodness for prognostics is measured by change in degradation. Most, if not all, of existing methods, analyze the feature change in the whole failure degradation. In other words, features collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be a good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents a methodology that divides the features into segments, each of which may have different goodness for prognostics. Thus, some part of the feature may be good, whereas the others not. The presented approach leads to extract more value from the features’ changing properties during the failure degradation. The method has been applied to simulated and real datasets obtained from Li-ion batteries aging tests. State of health (SoH) estimation accuracy is enhanced with the presented approach.
Feature Selection, Feature Evaluation, Time Series Segmentation, Feature fusion, SoH estimation, Prognostics, Remaining Useful Life
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