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Bearing is an essential component whose failure leads to costly downtime in operation. Therefore, it is important to establish an accurate health indicator (HI), using which the remaining useful life can be reliably predicted. To date, most of the health assessment for bearing have been focused on the constant operating condition while in practice, it operates under various operating conditions (rotating speed and loading). Motivated by this, this paper proposes a method to extract robust HI which undergoes variable operating conditions. The idea is to cluster the operating conditions regimes, and develop HI based on the Mahalanobis distance using the optimal features subset in each regime. To validate the effectiveness, bearing run-to-fail experiment is performed under variable operating condition, and proposed HI is compared with the traditional statistical features. The remaining useful life is predicted by the data augmentation prognostics algorithm which was to overcome data deficiency problem.

A Robust Health Indicator for Rotating Machinery Under Time-Varying Operating Conditions