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 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.

scholarly journals Machine remaining life prediction based on multi-layer self-attention and temporal convolution network

2021 ◽  
Author(s):  
Zhiwu Shang ◽  
Baoren Zhang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Jie Zhang
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Feature Fusion ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Statistical Features ◽  
Residual Structure ◽  
Dilated Convolution ◽  
Useful Life ◽  
Deep Learning Features

AbstractConvolution neural network (CNN) has been widely used in the field of remaining useful life (RUL) prediction. However, the CNN-based RUL prediction methods have some limitations. The receptive field of CNN is limited and easy to happen gradient vanishing problem when the network is too deep. The contribution differences of different channels and different time steps to RUL prediction are not considered, and only use deep learning features or handcrafted statistical features for prediction. These limitations can lead to inaccurate prediction results. To solve these problems, this paper proposes an RUL prediction method based on multi-layer self-attention (MLSA) and temporal convolution network (TCN). The TCN is used to extract deep learning features. Dilated convolution and residual connection are adopted in TCN structure. Dilated convolution is an efficient way to widen receptive field, and the residual structure can avoid the gradient vanishing problem. Besides, we propose a feature fusion method to fuse deep learning features and statistical features. And the MLSA is designed to adaptively assign feature weights. Finally, the turbofan engine dataset is used to verify the proposed method. Experimental results indicate the effectiveness of the proposed method.

Dilated Convolution Neural Network for Remaining Useful Life Prediction

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Xin Xu ◽  
Qianhui Wu ◽  
Xiu Li ◽  
Biqing Huang
Keyword(s):  
Life Prediction ◽  
High Performance ◽  
Economic Loss ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Sensor Data ◽  
Training Time ◽  
Dilated Convolution ◽  
Useful Life ◽  
Accuracy Of Prediction

Abstract Accurate prediction of remaining useful life (RUL) plays an important role in reducing the probability of accidents and lessening the economic loss. However, traditional model-based methods for RUL are not suitable when operating conditions and fault models are complicated. To deal with this problem, this paper proposes a novel data-driven method based on a deep dilated convolution neural networks (D-CNN). The novelties of the proposed method are triple folds. First, no feature engineering is required, and the raw sensor data are directly used as the input of the model. Second the dilated convolutional structure is used to enlarge the receptive field and further improve the accuracy of prediction. Finally, time sequences are encoded by a 2D-convolution to extract higher-level features. Extensive experiments on the C-MAPSS dataset demonstrate that the proposed D-CNN achieves high performance while requiring less training time.

scholarly journals A Time-Series Data Generation Method to Predict Remaining Useful Life

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1115
Author(s):  
Gilseung Ahn ◽  
Hyungseok Yun ◽  
Sun Hur ◽  
Si-Yeong Lim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Generation Model ◽  
Data Generation ◽  
Training Time ◽  
Symbolic Aggregate Approximation ◽  
Useful Life ◽  
Occurrence Patterns

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

scholarly journals A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

2021 ◽  
Vol 7 ◽  
pp. 5562-5574 ◽  
Author(s):  
Shunli Wang ◽  
Siyu Jin ◽  
Dekui Bai ◽  
Yongcun Fan ◽  
Haotian Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lithium Ion Batteries ◽  
Critical Review ◽  
Life Prediction ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Learning Methods ◽  
Useful Life

scholarly journals A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Zhiyuan Xie ◽  
Shichang Du ◽  
Jun Lv ◽  
Yafei Deng ◽  
Shiyao Jia
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Recurrent Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Second Phase ◽  
Learning Methods ◽  
Useful Life ◽  
Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

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