scholarly journals Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ran Wang ◽  
Ruyu Shi ◽  
Xiong Hu ◽  
Changqing Shen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Receptive Fields ◽  
Remaining Useful Life ◽  
Global Features ◽  
Complex Signals ◽  
Dilated Convolution ◽  
Local And Global Features ◽  
Useful Life

Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously is limited to conventional CNN. Thus, a multiscale convolutional neural network (MS-CNN) is introduced to overcome these aforementioned problems. Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. Then, several stacked integrated dilated convolution blocks in different depths are concatenated to extract local and global features. The effectiveness of the proposed method is verified by a bearing dataset prepared from the PRONOSTIA platform. The results turn out that the proposed MS-CNN has higher prediction accuracy than many other deep learning-based RUL methods.

NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ali Al-Dulaimi ◽  
Soheil Zabihi ◽  
Amir Asif ◽  
Arash Mohammed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Smart Manufacturing ◽  
Life Estimation ◽  
Noisy Input ◽  
Deep Architecture ◽  
Useful Life

Abstract Smart manufacturing and industrial Internet of things (IoT) have transformed the maintenance management concept from the conventional perspective of being reactive to being predictive. Recent advancements in this regard has resulted in development of effective prognostic health management (PHM) frameworks, which coupled with deep learning architectures have produced sophisticated techniques for remaining useful life (RUL) estimation. Accurately predicting the RUL significantly empowers the decision-making process and allows deployment of advanced maintenance strategies to improve the overall outcome in a timely fashion. In light of this, the paper proposes a novel noisy deep learning architecture consisting of multiple models designed in parallel, referred to as noisy and hybrid deep architecture for remaining useful life estimation (NBLSTM). The proposed NBLSTM is designed by integration of two parallel noisy deep architectures, i.e., a noisy convolutional neural network (CNN) to extract spatial features and a noisy bidirectional long short-term memory (BLSTM) to extract temporal information learning the dependencies of input data in both forward and backward directions. The two paths are connected through a fusion center consisting of fully connected multilayers, which combines their outputs and forms the target predicted RUL. To improve the robustness of the model, the NBLSTM is trained based on noisy input signals leading to significantly robust and enhanced generalization behavior. Through 100 Monte Carlo simulation runs performed under three different signal-to-noise ratio (SNR) values, it can be noted that utilization of the noisy training enhanced the results by reducing the standard deviation (std) between 9% and 67% across different settings in terms of the root-mean-square error (RMSE) and between 21% and 63% in terms of the score value. The proposed NBLSTM model is evaluated and tested based on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset provided by NASA, illustrating state-of-the-art results in comparison with its counterparts.

scholarly journals Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3035
Author(s):  
Feiyue Deng ◽  
Yan Bi ◽  
Yongqiang Liu ◽  
Shaopu Yang
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Operational Efficiency ◽  
Remaining Useful Life ◽  
Convolution Kernel ◽  
Training Time ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Convolution Kernels ◽  
Useful Life

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes to construct the network framework. There are two main shortcomings of this approach: (1) the convolution operation based on multiple size convolution kernels requires enormous computation and has a low operational efficiency, which severely restricts its application in practical engineering. (2) The convolutional layer with a large size convolution kernel needs a mass of weight parameters, leading to a dramatic increase in the network training time and making it prone to overfitting in the case of small datasets. To address the above issues, a multi-scale dilated convolution network (MsDCN) is proposed for RUL prediction in this article. The MsDCN adopts a new multi-scale dilation convolution fusion unit (MsDCFU), in which the multi-scale network framework is composed of convolution operations with different dilated factors. This effectively expands the range of receptive field (RF) for the convolution kernel without an additional computational burden. Moreover, the MsDCFU employs the depthwise separable convolution (DSC) to further improve the operational efficiency of the prognostics model. Finally, the proposed method was validated with the accelerated degradation test data of rolling element bearings (REBs). The experimental results demonstrate that the proposed MSDCN has a higher RUL prediction accuracy compared to some typical CNNs and better operational efficiency than the existing multi-scale CNNs based on different convolution kernel sizes.

scholarly journals Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yu Pang ◽  
Limin Jia ◽  
Zhan Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Efficiency ◽  
Remaining Useful Life ◽  
Discrete Cosine Transformation ◽  
Time Frequency ◽  
Cosine Transformation ◽  
Useful Life ◽  
Spectrum Coding ◽  
Low Dimensional

In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.

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