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An Adaptive Trend Index-Driven Remaining Useful Life Prediction Method . . . In those methods, the trend index construction is one of the most crucial steps Hence, an adaptive trend index-driven remaining useful life prediction method is proposed to conduct condition assessment and prediction of renewable energy vehicle reducers
AnAdaptiveTrendIndex-DrivenRemainingUsefulLife . . . In the proposed method, lots of life-cycle bearing data were used to train and test the proposed model e above- mentioned intelligent model-based RUL prediction method
An Adaptive Framework for Remaining Useful Life Prediction . . . - MDPI Abstract The prediction of Remaining Useful Life (RUL) constitutes a vital aspect of Prognostics and Health Management (PHM), providing capabilities for the assessment of mechanical component health status and prediction of failure instances
A comprehensive overview of remaining useful life prediction: From . . . The increasing complexity of industrial systems has heightened the need for precise Remaining Useful Life (RUL) predictions This paper provides a comprehensive overview of RUL prediction methods, processes, datasets, and tools, alongside insights from scientometric analysis
A New Dynamic Adaptive Prognostics Method Based on Comprehensive Model . . . Dynamic adaptive prognostics method is attracting increasing attention in remaining useful life (RUL) prediction of bearings, owing to selecting different models for prediction, and more accurately capturing the degradation trend However, existing research exhibits two shortcomings: 1) Existing research constructs model sets that are difficult to comprehensively describe the complex and
An adaptive remaining useful life prediction approach for single . . . The real-world battery dataset provided by NASA Ames research center is applied to verify the proposed RUL prediction approach Experimental results show that the proposed approach outperforms the existing conventional data-driven approaches for predicting the battery’s RUL
Adaptive deep learning-based remaining useful life prediction framework . . . This work proposes an adaptive deep learning-based RUL prediction framework with FM recognition First, a FM recognizer fusing physics-informed FM classifier with deep convolutional neural networks (DCNN) is developed, which improves the interpretability and the accuracy of the recognition model