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

Wang, Ran; Shi, Ruyu; Xiong, Hu; Shen, Changqing

doi:10.1155/2021/6616861

Cited by 28 publications

(13 citation statements)

References 27 publications

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“…This is accomplished by enabling sparse interactions between input data and trainable parameters by using parameter sharing in order to learn equivariant representations (also called feature maps) of complex and spatially structured input information [58]. In a Deep CNN, units in the deeper layers may indirectly interact with a large portion of input due to the usage of pooling operations that replaces the output of Net at a certain location with a summary statistic and allows the network to learn complex features from this compressed representation [49]. The so-called "top" of the CNN is usually composed of a bunch of fully connected layers, including the output layer, which uses the complex features learned by previous layers to make predictions.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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“…In the problem of predicting the remaining life of mechanical equipment based on a supervised learning model, adding labels to the data is equivalent to modeling the degradation state of the equipment. At present, there are two main ways to add labels [38][39][40][41][42]. One is to use a linear function, as shown in Figure 12a.…”

Section: Prediction Of the Rul Based On 1d-cnnmentioning

confidence: 99%

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

et al. 2021

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Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

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“…The different scale of the convolution kernel impacts the network's learning ability. Gene2Vec [ 30 ] and DeepPromise [ 12 ] directly used CNN composed of a single-scale convolution kernel, which might lead to incomplete representation learning of sequences [ 36 ]. The missing information in both methods may be important to the final site prediction.…”

Section: Introductionmentioning

confidence: 99%

EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction

et al. 2022

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Background Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious. Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons. Results This study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction. The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m1A and m6A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php (http://47.104.130.81/EMDLP/index.php). Conclusions We developed a predictor for m1A and m6A methylation sites.

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

Cited by 28 publications

References 27 publications

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction

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