Full Reproduction of Surface Dynamometer Card Based on Periodic Electric Current Data

2021 ◽  
pp. 1-10
Author(s):  
Dandan Zhu ◽  
Xiaoting Luo ◽  
Zhanmin Zhang ◽  
Xiangyi Li ◽  
Gao Peng ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Current Method ◽  
Current Data ◽  
Oil Well ◽  
Financial Investment ◽  
Pumping System ◽  
The Mean ◽  
Lstm Network ◽  
Well Data ◽  
Dynamometer Card

Summary The surface dynamometer card is composed of ground load and ground displacement, which is of great significance to reflect the operation of rod pumping and the exploitation of crude oil. However, the current method of obtaining the surface dynamometer by sensors is a huge financial investment on the sensor installations and maintenance. In this paper, we propose an innovative method based on deep learning to reproduce the surface dynamometer card directly from electrical parameters. In our method, the convolution neural network is used as the basic layer to automatically extract the spatial characteristics of input data. A long short-term memory (LSTM) network as the core component is used for the output layer to consider the time dependence of the dynamometer card. Finally, the experimental shows that the proposed method achieves the mean relative error (MRE) of 4.00% on the real oil well data in A-oilfield, and the dynamometer card calculated by our model is basically consistent with the field data. In addition, the method has been tested in new wells with a rod pumping system, and the results show that the accuracy of the model is close to 90%, which has already greatly outperformed the previous methods.

scholarly journals Application of CNN-LSTM in Gradual Changing Fault Diagnosis of Rod Pumping System

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yanfeng He ◽  
Yali Liu ◽  
Shuai Shao ◽  
Xuhang Zhao ◽  
Guojun Liu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Short Term Memory ◽  
Fault Classification ◽  
Training Dataset ◽  
Irreversible Damage ◽  
Indicator Diagram ◽  
Well Production ◽  
Pumping System ◽  
Lstm Network ◽  
Model Diagnosis

Owing to the importance of rod pumping system fault detection using an indicator diagram, indicator diagram identification has been a challenging task in the computer-vision field. The gradual changing fault is a special type of fault because it is not clearly indicated in the indicator diagram at the onset of its occurrence and can only be identified when an irreversible damage in the well has been caused. In this paper, we proposed a new method that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to perform a gradual changing fault classification. In particular, we employed CNN to extract the indicator diagram multilevel abstraction features based on its hierarchical structure. We considered the change in the time series of indicator diagrams as a sequence and employed LSTM to perform recognition. Compared with traditional mathematical model diagnosis methods, CNN-LSTM overcame the limitations of the traditional mathematical model theoretical analysis such as unclear assumption conditions and improved the diagnosis accuracy. Finally, 1.3 million sets of well production were set as a training dataset and used to evaluate CNN-LSTM. The results demonstrated the effectiveness of utilizing CNN and LSTM to recognize a gradual changing fault using the indicator diagram and characteristic parameters. The accuracy reached 98.4%, and the loss was less than 0.9%.

A Fault Diagnosis of Suck Rod Pumping System Based on Wavelet Packet and RBF Network

2011 ◽  
Vol 189-193 ◽  
pp. 2665-2669 ◽  
Author(s):  
Wei Wu ◽  
Wen Li Sun ◽  
Hang Xin Wei
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Oil Well ◽  
Rbf Network ◽  
Pumping System ◽  
Fault Type ◽  
The Neural Network ◽  
Safety Protection ◽  
Diagnosis Method ◽  
Dynamometer Card

In order to diagnose the fault of the pump oil well, a fault diagnosis method based on the wavelet packet and the neural network is introduced. Firstly, the dynamometer card of the pumping well is gathered and normalized, then these data are decomposed by using three layers of wavelet packet. It makes up eight energy eigenvectors which are regarded as the input eigenvector of the RBF network. The experiment indicates that the method can not only detect the fault of the pumping oil well but also can recognize the fault type of it. It shows that the method is very effective for safety protection and fault diagnosis in the pumping oil well.

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

A Fault Diagnosis of Suck Rod Pumping System Based on SVM

2013 ◽  
Vol 307 ◽  
pp. 285-289 ◽  
Author(s):  
Wei Wu ◽  
Yu Zhou ◽  
Hang Xin Wei
Keyword(s):  
Fault Diagnosis ◽  
Support Vector ◽  
Oil Well ◽  
Pump System ◽  
Indicator Diagram ◽  
Invariant Moments ◽  
Pumping System ◽  
System A ◽  
Fault Type ◽  
Shape Characteristics

Aiming at the defects of fault diagnosis in the traditional method for sucker rod pump system, a new method based on support vector machine (SVM) pump fault diagnosis is proposed. Through studying the theory of invariant moment and the shape characteristics of pump indicator diagram, seven invariant moments is extracted from the indicator diagram as a pumping unit well condition of the characteristic parameters. Then these parameters are pretreatment, and it makes up seven eigenvector which are regarded as the input eigenvector of the SVM. The experiment indicates that the method can not only detect the fault of the pumping oil well but also can recognize the fault type of it, which is very effective for safety protection and fault diagnosis of the pumping oil.

scholarly journals A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1181
Author(s):  
Chenhao Zhu ◽  
Sheng Cai ◽  
Yifan Yang ◽  
Wei Xu ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Error Compensation ◽  
Random Vibration ◽  
Short Term Memory ◽  
Combined Method ◽  
Short Term ◽  
Mems Gyroscope ◽  
Long Short Term Memory ◽  
Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

scholarly journals Antibody design using LSTM based deep generative model from phage display library for affinity maturation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koichiro Saka ◽  
Taro Kakuzaki ◽  
Shoichi Metsugi ◽  
Daiki Kashiwagi ◽  
Kenji Yoshida ◽  
...  
Keyword(s):  
Phage Display ◽  
Short Term Memory ◽  
Current Method ◽  
Phage Display Library ◽  
Affinity Maturation ◽  
Generative Model ◽  
Sequence Generation ◽  
Machine Learning Approach ◽  
Antibody Design ◽  
Ngs Data

AbstractMolecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)—a widely used deep generative model—based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.

scholarly journals Survey of general practitioners' attitudes towards psychiatry

2010 ◽  
Vol 34 (12) ◽  
pp. 525-528 ◽  
Author(s):  
Catherine Thompson ◽  
Nisha Dogra ◽  
Robert McKinley
Keyword(s):  
General Practitioners ◽  
Role Models ◽  
Positive Attitude ◽  
Demographic Data ◽  
Current Data ◽  
Undergraduate Teaching ◽  
Individual Response ◽  
Positive Attitudes ◽  
The Mean ◽  
Teaching Psychiatry

Aims and methodThere is a lack of current data regarding attitudes of doctors towards psychiatry. General practitioners (GPs) are increasingly involved in teaching psychiatry, and their attitudes towards psychiatry may affect their ability to promote psychiatry. The main aim of the study is to inform on current attitudes of GPs towards psychiatry as a discipline. The Attitudes Towards Psychiatry (ATP-30) questionnaire was administered to all GPs within Shropshire.ResultsThe response rate was 61% (n = 145 from N = 239). The mean score for the ATP-30 was 113.9. An association was found between GP trainer status and higher ATP-30 scores. Positive associations were found between demographic data (age, length of career, postgraduate experience of psychiatry, involvement in undergraduate teaching, GP trainer status) and individual response items on the ATP-30 scale.Clinical implicationsGeneral practitioners in Shropshire have a positive attitude towards psychiatry. Associations between demographic data and ATP-30 scores indicate that GPs with more experience of psychiatry and those involved in training may have more positive attitudes. The main limitation of the study is the lack of proven validity of the scale for use in this population. The positive attitude towards psychiatry is consistent with GPs providing the role models needed if they are to be involved to a greater degree with teaching and promoting psychiatry as a career. The need for the development of a more specific tool or an update to the existing tool is discussed.

scholarly journals Detection of dementia on raw voice recordings using deep learning: A Framingham Heart Study

2021 ◽  
Author(s):  
Chonghua Xue ◽  
Cody Karjadi ◽  
Ioannis Ch. Paschalidis ◽  
Rhoda Au ◽  
Vijaya B. Kolachalama
Keyword(s):  
Deep Learning ◽  
Framingham Heart Study ◽  
Short Term Memory ◽  
Neuropsychological Testing ◽  
Heart Study ◽  
Audio Recordings ◽  
Lstm Network ◽  
Sensitivity Specificity ◽  
Normal Cognition ◽  
Mean Sensitivity

AbstractBackgroundIdentification of reliable, affordable and easy-to-use strategies for detection of dementia are sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data without any pre-processing are not readily available.MethodsWe used a subset of 1264 digital voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 minutes in duration, on average, and contained at least two speakers (participant and clinician). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia. We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the raw audio recordings to classify if the recording included a participant with only NC or only dementia, and also to differentiate between recordings corresponding to non-demented (NC+MCI) and demented participants.FindingsBased on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the sensitivity-specificity curve (AUC) of 0.744±0.038, mean accuracy of 0.680±0.032, mean sensitivity of 0.719±0.112, and mean specificity of 0.652±0.089 in predicting cases with dementia from those with normal cognition. The CNN model achieved a mean AUC of 0.805±0.027, mean accuracy of 0.740±0.033, mean sensitivity of 0.735±0.094, and mean specificity of 0.750±0.083 in predicting cases with only dementia from those with only NC. For the task related to classification of demented participants from non-demented ones, the LSTM model achieved a mean AUC of 0.659±0.043, mean accuracy of 0.701±0.057, mean sensitivity of 0.245±0.161 and mean specificity of 0.856±0.105. The CNN model achieved a mean AUC of 0.730±0.039, mean accuracy of 0.735±0.046, mean sensitivity of 0.443±0.113, and mean specificity of 0.840±0.076 in predicting cases with dementia from those who were not demented.InterpretationThis proof-of-concept study demonstrates the potential that raw audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can provide a level of screening for dementia.

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