An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump

Bie, Fengfeng; Du, Tengfei; Lyu, Fengxia; Pang, Mingjun; Guo, Yue

doi:10.1109/access.2021.3056437

Cited by 23 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…State changes in classical repetitive neural networks are one way. However, in some cases, the output of the current state is not only related to the previous state but also related to the next state [15]. For example, the prediction of missing words in a sentence must not only determine the preposition but also the meaning of the text that follows, and the emergence of bidirectional recurrent neural networks solves this problem.…”

Section: Text Feature Extraction Based On Bidirectional Grumentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

show abstract

Section: Text Feature Extraction Based On Bidirectional Grumentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…For the sentiment analysis of sentence text, Hanbay [15] applied DL technology to it, built a recursive neural network to build an analysis tree of sentence grammar, and added the grammatical information of the whole sentence as a feature to the training of the model [15]. Bie et al [16] proposed a two-way gate unit recurrent neural network model of hierarchical attention mechanism for multiple-choice reading comprehension tasks, which introduced the hierarchical structure of documents so that context, questions, and candidates interacted at word and sentence level [16]. Zhang et al [17] improved the coding layer and reasoning layer in the previous machine reading comprehension model: vocabulary and syntax features were integrated into the coding layer, and self-matching of documents was realized in the reasoning layer, and a memory-based answer extraction network was proposed, which performed well in segment extraction tasks [17].…”

Section: Dlnn-related Research DL (Deep Learningmentioning

confidence: 99%

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

Sheng

Gao

2022

Mobile Information Systems

View full text Add to dashboard Cite

The demand for talents extends beyond theoretical knowledge to include students’ IPE (Ideological and Political Education). We can only continue to promote the rapid development of university teaching in China by putting in place a solid management system. It is the foundation of college students’ IPE in this era of rapid information technology development to accurately adapt to and understand the actual problems and requirements of IPE proposed by the development of the times and innovate and develop accordingly. Deep learning (DL) is a sophisticated machine learning algorithm that outperforms previous technologies in speech and image recognition. In this paper, DLNN (deep learning neural network) technology is applied to IPE evaluation and a set of university-specific IPE evaluation index systems is developed. The BPNN (BP Neural Network) evaluation model is used to train and study a specific amount of teaching quality data in the MATLAB simulation tool. On the prediction of IPE evaluation, the BPNN algorithm has a prediction error of about 0.050.4 according to the findings. The validity and accuracy of using the BPNN algorithm to model IPE quality are demonstrated.

show abstract

“…They fused time and frequency domain features obtained from denoised signals by EWT and further used as input to CNN for effective fault diagnosis. In other work, Bie et al 2021 154 used singular spectral entropy of intrinsic modal function (IMF components of vibration signals as input to LSTM DNN for performing successful diagnosis of reciprocating pump.…”

Section: Based Rotating Machines Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

View full text Add to dashboard Cite

Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

show abstract

An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump

Cited by 23 publications

References 20 publications

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Contact Info

Product

Resources

About