scholarly journals A Decision-Making Method for Machinery Abnormalities Based on Neural Network Prediction and Bayesian Hypothesis Testing

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1610
Author(s):  
Gaojun Liu ◽  
Shan Yang ◽  
Gaixia Wang ◽  
Fenglei Li ◽  
Dongdong You
Keyword(s):  
Neural Network ◽  
Hypothesis Testing ◽  
Nuclear Power ◽  
Short Term Memory ◽  
Failure Time ◽  
Open Data ◽  
Signal Prediction ◽  
Bayesian Hypothesis Testing ◽  
Machinery Condition Monitoring ◽  
Neural Network Prediction

For anomaly identification of predicted data in machinery condition monitoring, traditional threshold methods have problems during residual testing. It is difficult to make decisions when the residuals are close to the threshold and fluctuate. This paper proposes a Bayesian dynamic thresholding method that combines Bayesian inference with neural network signal prediction. The method makes full use of historical prior data to build an anomaly identification and warning model applicable under single variable or multidimensional variables. A long short-term memory signal prediction model is established, and then a Bayesian hypothesis testing-based anomaly identification strategy is presented to quantify the probability of anomaly occurrence and issue early warnings for anomalies beyond a certain probability. The model was applied to open data sets of a pumping station and actual operating data of a nuclear power turbine. The results indicate that the model successfully predicts the failure probability and failure time. The effectiveness of the proposed method is verified.

scholarly journals The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

2020 ◽  
Vol 10 (16) ◽  
pp. 5622
Author(s):  
Zitong Zhou ◽  
Yanyang Zi ◽  
Jingsong Xie ◽  
Jinglong Chen ◽  
Tong An
Keyword(s):  
Neural Network ◽  
Dynamic Time Warping ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Failure Time ◽  
Low Complexity ◽  
Time Warping ◽  
Average Prediction Error ◽  
Time Prediction ◽  
Dynamic Time

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month.

scholarly journals Research on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11 ◽  
Author(s):  
Yusheng Chen ◽  
Meng Lin ◽  
Ren Yu ◽  
Tianshu Wang
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Short Term Memory ◽  
Grey Theory ◽  
Operation Conditions ◽  
Comparison Results ◽  
Great Pressure ◽  
Long Short Term Memory

The nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex. In the case of an operation fault in these systems, there will be a large number of alarm parameters, which can cause humans to be hurt in the accidents under great pressure. Therefore, it is necessary to predict the values of the key parameters of a device system. The prediction of the key parameters’ values can help operators determine the changing trends of system parameters in advance, which can effectively improve system safety. In this paper, a deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. The proposed network is verified by simulations and compared with the traditional grey theory. The simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant.

Efficient Secure Neural Network Prediction Protocol Reducing Accuracy Degradation

2020 ◽  
Vol E103.A (12) ◽  
pp. 1367-1380
Author(s):  
Naohisa NISHIDA ◽  
Tatsumi OBA ◽  
Yuji UNAGAMI ◽  
Jason PAUL CRUZ ◽  
Naoto YANAI ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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