End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis

Khorram, Amin; Khalooei, Mohammad; Rezghi, Mansoor

doi:10.1007/s10489-020-01859-1

Cited by 107 publications

(55 citation statements)

References 28 publications

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“…is method is based on macrolevel information, such as the company's financial and operating conditions. Experts rely on this macroinformation, coupled with personal experience and judgment, to realize the prediction and inference of the future trend of the stock [25][26][27].…”

Section: Market Stock Indexmentioning

confidence: 99%

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

2021

Security and Communication Networks

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As one of the most popular financial management methods, stocks have attracted more and more investors to participate. The risks of stock investment are relatively high. How to reduce risks and increase profits has become the most concerned issue for investors. Traditional stock forecasting models use forecasting models based on stock time series analysis, but time series models cannot consider the influence of investor sentiment on stock market changes. In order to use investor sentiment information to make more accurate stock market forecasts, this paper establishes a stock index forecast and network security model based on time series and deep learning. Based on the time series model, it is proposed to use CNN to extract in-depth emotional information to replace the basic emotional features of the emotional extraction level. At the data source level, other information sources, such as basic features, are introduced to further improve the predictive performance of the model. The results show that the algorithm is feasible and effective and can better predict the changes in the market stock index. This also proves that multiple information sources can improve the accuracy of model prediction more effectively than a single information source.

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Section: Market Stock Indexmentioning

confidence: 99%

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

2021

Security and Communication Networks

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“…e CWT-CNN-RF method proposed in reference [40] has good results in 10 kinds of data, but its processing process is more complicated. Compared with the method that directly used the original signal as the input in literature [29,32,38,39], the CNN-BLSTM method proposed in this paper can effectively extract the fault features and identify the fault state in a shorter time.…”

Section: Cnn-blstm Modelmentioning

confidence: 99%

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

You

Chen

Liu

et al. 2021

Shock and Vibration

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The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

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“…For this reason, these sensors measurements are removed from the input data. In this study, it is preferred 14 sensor measurements consisting of 2, 3,4,7,8,9,11,12,13,14,15,17,20, and 21 as the raw input data. Besides, the data gathered by the different sensors are standardized to be in the range [0, 1] utilizing the Min-Max scaling technique by Equation 8.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Recently, several prognostic approaches based on deep neural networks have been extensively introduced in order to capture complex and nonlinear patterns from degradation data, and these 217 approaches have significantly increased the efficiency and reliability of RUL prediction. Convolutional neural network (CNN) [8], long-short term memory (LSTM) [9], gated recurrent unit (GRU) [10], and some hybrid methods [11], [12] have been implemented to take advantage of their superiority. For instance, Chen et al [13] built an encoder-decoder structure consisting of CNN, bidirectional GRU, and attention mechanism for bearing RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prognostic Approach Based on Deep Learning for the Degradation Prediction of Machinery

Kara

2021

Sakarya University Journal of Computer and Information Sciences

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Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a datadriven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models.

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End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis

Cited by 107 publications

References 28 publications

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

A Hybrid Prognostic Approach Based on Deep Learning for the Degradation Prediction of Machinery

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