Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings

By Rodney K. Singleton, Elias G. Strangas, Selin Aviyente. IEEE transactions on industrial electronics (1982. Print), 2015

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A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem (2016, background): “As it does not need to know the exact failure mechanisms during modeling, datadriven approach is becoming more appealing in recent years [18]–[20].”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “2) Although two-direction vibration signals are collected, the vertical vibration signals provide less useful information than the horizontal ones for tracking the bearing degradations according to the related literature [28, 29].”
Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “Some new PHIs were also developed from the entropy of time-domain signals [46,51,68,84,90,124].”
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): “As a result, the theoretical lifetime of a mechanical equipment can be inﬂuenced greatly by accidental error and noise, and therefore be different from the actual RUL [18].”