Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network

2016 ◽  
Author(s):  
Mei Yuan ◽  
Yuting Wu ◽  
Li Lin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Aero Engine ◽  
Useful Life

Recurrent Neural Network Based on Statistical Recurrent Unit for Remaining Useful Life Estimation

2019 ◽  
Author(s):  
Andre Ribeiro de Miranda ◽  
Talles M. G. de Andrade Barbosa ◽  
Andre G. Scolari Conceicao ◽  
Symone G. Soares Alcala
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Useful Life

scholarly journals Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shuang Zhou ◽  
Maohua Xiao ◽  
Petr Bartos ◽  
Martin Filip ◽  
Guosheng Geng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Fault Identification ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Useful Life ◽  
Short Time

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification.

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