Many techniques for prognostics depend on estimating then forecasting health indicators that reflect the overall health or performance of an asset. For vibration data, health indicators are typically calculated by combining various vibration measurements along with derived features extracted from time, frequency or time-frequency domain analysis. However, selecting or handcrafting good features is a labor-intensive task. On the other hand, deep learning models might be able to learn health indicators automatically from vibration data but require large amount of training data, which are typically hard to obtain from real assets. In this paper, we propose an innovative similarity-based feature extraction method for vibration data which can then be used to learn health indicators and estimate remaining useful life of equipment. The method learns a set of representative templates of frequency spectra for both normal and failure states, and then calculates similarity-based features between new vibration data and the set of learned templates. These features are used to estimate health indicators which are then extrapolated to estimate the future health condition of the asset and its remaining useful life. The proposed method has been tested on the PRONOSTIA bearing dataset provided by FEMTO-ST Institute and achieved a higher accuracy in estimating the remaining useful life of bearings compared to other studies. The results demonstrate the effectiveness of the proposed method for assets with limited training data.
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Prognostics, similarity-based feature extraction, vibration data, bearing failures
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