An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

By Naipeng Li, Yaguo Lei, Jing Lin, Steven X. Ding. IEEE transactions on industrial electronics (1982. Print), 2015

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Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[62] proposed a continuous trigger mechanism to reduce the interference of random noise.”
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “[6], [22], and particle filtering (PF) [23], [24], are employed to identify and update model parameters and predict RUL of bearings.”
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, background): “Degradation patterns on even identical complements vary due to different operational conditions, materials, manufacturing, or processing techniques [4].”
RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model (2017, background): “Examples include the capacity of batteries [1], output light of light-emitting diodes (LED) and displays [2], [3], and vibration of rolling bearings [4], [5].”
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, background): “In engineering practice, the collected vibration signals are always complex and nonstationary with heavy noise [15].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “, 2005) and further developed and applied to bearing prognostics (Li et al., 2015) and battery prognostics (He et al.”