Gas pipeline event classification based on one-dimensional convolutional neural network

2021 ◽  
pp. 147592172110102
Author(s):  
Yang An ◽  
Xueyan Ma ◽  
Xiaocen Wang ◽  
Zhigang Qu ◽  
Xixin Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Gas Pipeline ◽  
Economic Losses ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Memory Network

Pipeline block and pipeline leak may lead to serious accidents and cause huge economic losses, which have been urgent problems for gas transportation. In this work, active acoustic pulse-compression technology is first introduced to detect and locate these two anomalous events. The matched filtered signals are then normalized and input into one-dimensional convolutional neural network to achieve classification of not only pipeline block and pipeline leak but also normal event such as pipeline elbow which causes acoustic wave reflection as well. Neural network parameter optimization has also been carried out as well as the comparison with long- and short-term memory network. Experimental results demonstrate that compared with long- and short-term memory network, one-dimensional convolutional neural network has an improvement in efficiency due to the great reduction of running time. For non-aliasing pipeline events, both of the models can reach 100% classification accuracy. For aliasing pipeline events, despite the shorter time series and fewer features, the classification accuracy of one-dimensional convolutional neural network still reaches 100.00%, but that of long- and short-term memory network is only 93.89%. Furthermore, the smoothing and slight fluctuation of receiver operating characteristic curve and the high value of area under curve also verify the stability and good classification performance of the proposed trained model. Therefore, the one-dimensional convolutional neural network shows significant performance for pipeline events classification and has considerable potential and application prospect in gas pipeline safety monitoring.

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

scholarly journals A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

2022 ◽  
Vol 12 (1) ◽  
pp. 721
Author(s):  
Ahmed Nasser ◽  
Huthaifa AL-Khazraji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

scholarly journals A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 261
Author(s):  
Wenxing Lu ◽  
Haidong Rui ◽  
Changyong Liang ◽  
Li Jiang ◽  
Shuping Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Flow Prediction ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Tourist Flow

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

scholarly journals Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Oishee Mazumder ◽  
Rohan Banerjee ◽  
Dibyendu Roy ◽  
Ayan Mukherjee ◽  
Avik Ghose ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Cardioverter Defibrillator ◽  
Wearable Cardioverter Defibrillator ◽  
Memory Network ◽  
Long Short Term Memory

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm. WCD guidelines dictate very high reliability and accuracy to deliver timely and optimal therapy. Computational model-based process validation can verify device performance and benchmark the device setting to suit personalized requirements. In this article, we present a computational pipeline for WCD validation, both in terms of shock classification and shock optimization. For classification, we propose a convolutional neural network-“Long Short Term Memory network (LSTM) full form” (Convolutional neural network- Long short term memory network (CNN-LSTM)) based deep neural architecture for classifying shockable rhythms like Ventricular Fibrillation (VF), Ventricular Tachycardia (VT) vs. other kinds of non-shockable rhythms. The proposed architecture has been evaluated on two open access ECG databases and the classification accuracy achieved is in adherence to American Heart Association standards for WCD. The computational model developed to study optimal electrotherapy response is an in-silico cardiac model integrating cardiac hemodynamics functionality and a 3D volume conductor model encompassing biophysical simulation to compute the effect of shock voltage on myocardial potential distribution. Defibrillation efficacy is simulated for different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. While the biophysical simulation provides the field distribution through Finite Element Modeling during defibrillation, the hemodynamic module captures the changes in left ventricle functionality during an arrhythmic event. The developed computational model, apart from acting as a device validation test-bed, can also be used for the design and development of personalized WCD vests depending on subject-specific anatomy and pathology.

Real-time fault diagnosis using deep fusion of features extracted by parallel long short-term memory with peephole and convolutional neural network

2020 ◽  
pp. 095965182094829
Author(s):  
Funa Zhou ◽  
Zhiqiang Zhang ◽  
Danmin Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Two Dimensional ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

Analysis of one-dimensional vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in one-dimensional sequence data is crucial for the accuracy of real-time fault diagnosis. This article aims to develop more effective means of extracting useful features potentially involved in one-dimensional vibration signals. First, an improved parallel long short-term memory called parallel long short-term memory with peephole is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It can not only solve the memory bottleneck problem of traditional long short-term memory for long sequence but also can make full use of all possible information helpful for feature extraction. Second, a fusion network with new training mechanism is designed to fuse features extracted from parallel long short-term memory with peephole and convolutional neural network, respectively. The fusion network can incorporate two-dimensional screenshot image into comprehensive feature extraction. It can provide more accurate fault diagnosis result since two-dimensional screenshot image is another form of expression for one-dimensional vibration sequence involving additional trend and locality information. Finally, real-time two-dimensional screenshot image is fed into convolutional neural network to secure a real-time online diagnosis which is the primary requirement of the engineers in health monitoring. Validity of the proposed method is verified by fault diagnosis for rolling bearing and gearbox.

scholarly journals Fault Diagnosis for Bearing Based on 1DCNN and LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Haibin Sun ◽  
Shichao Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Memory Network ◽  
Long Short Term Memory

Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.

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