Dynamic time scales ensemble framework for similarity-based remaining useful life prediction under multiple failure modes

Abstract

In modern industry, the stochastic degradation of mechanical equipment typically involves multiple failure modes, which heavily affects the reliability of the remaining useful life (RUL) prediction. The similarity-based methods have been widely deployed in RUL prediction due to their flexibility, but it is still challenging to accurately identify similar degradation trajectories under varying failure modes. The obstacles lie in the interference of reference trajectories under different degradation states and the insufficiency of measuring trajectory trends. Therefore, this paper proposes a dynamic scales ensemble method based on the mean removal Canberra distance with failure identification (FI-MRC-DSE) for similarity-based prognosis. Firstly, a gated recurrent unit autoencoder network is employed to adaptively extract failure features from multi-dimensional monitoring data to support the targeted selection of reference trajectories. Then, the similarity matching is performed based on the proposed MRC distance instead of the commonly used Euclidean distance, enhancing the perception of degradation trends. Finally, the matching results across multiple time scales, which are dynamically determined by the instance's degradation state, are integrated to obtain the predicted RUL. It effectively overcomes the insufficient utilization of trajectory caused by the single time scale. In the experiments, the superiority of our developed similarity-based FI-MRC-DSE method is demonstrated by comparison with the state-of-the-art similarity-based methods. The effectiveness analyses and the ablation study show that all three key components contribute to accurate prognosis under multiple failure modes.