Remaining useful life estimation in prognostics using deep convolution neural networks

By Xiang Li, Qian Ding, Jianqiao Sun. Reliability Engineering & System Safety, 2018

1,516 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/809003357de849d443a37b29996e2130c7bdb041

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “…methods have been proposed to prognostics problems containing high amounts of time-series input data, including Convolutional Neural Networks (CNN) [10] and variations of RNNs capable of dealing with long input sequences such as the Long Short-Term Memory Networks (LSTM)[11, 12] and Gated…”
The use of Digital Twin for predictive maintenance in manufacturing (2019, background): “Prognostics and Health Management (PHM) is an engineering discipline, focused on predicting the time when a system or a component will no longer perform its intended function (Li, Ding, and Sun 2018).”
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “[7] achieved slightly higher RMSE prediction accuracy on subset FD002.”
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021, methodology): “Some recent examples that are also among the best performing prognostics models applied to the CMAPSS dataset are deep learning based methods such as convolutional neural network (CNN) [12,13], long short-term memory networks (LSTM) [14–19] or hybrid networks combining CNN and LSTM layers [20,21].”
Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks (2019, background): “A number of deep learning-based methods have been successfully developed in fault diagnosis research works [9]–[11].”
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, background): “Again, [24], [91]–[93], and [94] are some of the published works which use CNN architecture for MFDD using a dataset other than the CWRU bearing dataset”
Fusing Physics-based and Deep Learning Models for Prognostics (2020, methodology): “Motivated by the main study, this final architecture is the result of conducting a grid search with the following search space: number of hidden layers [5-6], number of neurons at each hidden layer [50, 100], and activation function type [tanh, ReLu].”