Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction

by   Yuwen Qin, et al.

The precise estimate of remaining useful life (RUL) is vital for the prognostic analysis and predictive maintenance that can significantly reduce failure rate and maintenance costs. The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction. The Temporal deep degradation network (TDDN) model is proposed to make the RUL prediction with the degradation-related features given by the one-dimensional convolutional neural network (1D CNN) feature extraction and attention mechanism. 1D CNN is used to extract the temporal features from the streaming sensor data. Temporal features have monotonic degradation trends from the fluctuating raw sensor streaming data. Attention mechanism can improve the RUL prediction performance by capturing the fault characteristics and the degradation development with the attention weights. The performance of the TDDN model is evaluated on the public C-MAPSS dataset and compared with the existing methods. The results show that the TDDN model can achieve the best RUL prediction accuracy in complex conditions compared to current machine learning models. The degradation-related features extracted from the high-dimension sensor streaming data demonstrate the clear degradation trajectories and degradation stages that enable TDDN to predict the turbofan-engine RUL accurately and efficiently.


page 15

page 21

page 23

page 26


Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting

Battery discharge capacity forecasting is critically essential for the a...

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are cause...

A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering

Reed relay serves as the fundamental component of functional testing, wh...

A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method

Geometrical shape of airfoils, together with the corresponding flight co...

Tracking and Visualizing Signs of Degradation for an Early Failure Prediction of a Rolling Bearing

Predictive maintenance, i.e. predicting failure to be few steps ahead of...

Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction

Accurate estimation of remaining useful life (RUL) of industrial equipme...

Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry

In the pharmaceutical industry, the maintenance of production machines m...

Please sign up or login with your details

Forgot password? Click here to reset