A hybrid fault diagnosis scheme for railway point machines by motor current signal analysis

2021 ◽  
pp. 095440972110619
Author(s):  
Khadem Hossaini Narges ◽  
Mirabadi Ahmad ◽  
Gholami Manesh Fereydoun
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Decision Tree ◽  
Network Architecture ◽  
Failure Modes ◽  
Support Vector ◽  
Current Signal ◽  
Neural Network Architecture ◽  
Mechanical Faults ◽  
Neural Network Algorithm

Proper analysis of point machine current signal provides pervasive information of health status of their internal components. Point machines are subjected to several failure modes during their operation. “Gearbox,” “ball bearing,” “lead screw,” and “sliding chair” faults are among common mechanical failure modes. In this article, a two-stage prediction innovative process is proposed using Fault Detection based Decision Tree strategy (FDDT) where the healthy and faulty modes are first determined, followed by classifying the types of mechanical faults based on Parallel Neural Network Architecture and Fuzzy System (PNNFS). To differentiate between faulty and healthy point machines, some relevant features are extracted from the motors’ current signals which are used as input data for the proposed FDDT_PNNFS method. Feature selection has been performed using the ReliefF to select the dominant predictors in the point machine. Firstly, the Decision Tree (DT) algorithm is used to obtain a classifier model based on the offline training method for fault detection. The performance of DT is compared with the support vector machine algorithm. In the second stage, faulty data is fed to a bank of Neural Networks, designed in Parallel Neural Network Architecture (PNNA), which is used for identifying the type of failures. Each Neural Network Algorithm (NNA) is responsible for detecting only one type of failure and assessment of the NNA outputs shows the final failure of the point machine. If there is a discrepancy between the outputs of the NNAs, fuzzy logic plays the role of modifier and judges among outputs of NNAs and determines the more probable fault type.

scholarly journals Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

10.2196/14502 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14502
Author(s):  
Po-Ting Lai ◽  
Wei-Liang Lu ◽  
Ting-Rung Kuo ◽  
Chia-Ru Chung ◽  
Jen-Chieh Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Disease Association ◽  
Support Vector ◽  
Context Aware ◽  
Neural Network Architecture ◽  
Large Margin

Background Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. Objective As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. Methods In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. Results Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. Conclusions To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.

Medical Image Diagnosis of Liver Cancer Using a Neural Network and Artificial Intelligence

2011 ◽  
Vol 15 (6) ◽  
pp. 714-722 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Shoichiro Takao
Keyword(s):  
Neural Network ◽  
Liver Cancer ◽  
Network Architecture ◽  
Medical Image ◽  
Feedback Loop ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Medical Image Diagnosis ◽  
Image Diagnosis ◽  
Neural Network Algorithm

A revised Group Method of Data Handling (GMDH)-type neural network algorithm using artificial intelligence technology for medical image diagnosis is proposed and is applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image diagnosis is used in organizing the neural network architecture for medical image diagnosis. Furthermore, the revisedGMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. The neural network architecture that optimally fit the complexity of the medical images, is automatically organized so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). It is shown that the revised GMDH-type neural network is accurate and a useful method for the medical image diagnosis of the liver cancer.

scholarly journals Detection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture

2021 ◽  
Vol 38 (6) ◽  
pp. 1783-1791
Author(s):  
Ali Arshaghi ◽  
Mohsen Ashourin ◽  
Leila Ghabeli
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Agricultural Products ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Defect Classification ◽  
Neural Network Architecture ◽  
Surface Image ◽  
Non Destructive

Using machine vision and image processing as a non-destructive and rapid method can play an important role in examining defects of agricultural products, especially potatoes. In this paper, we propose a convolution neural network (CNN) to classify the diseased potato into five classes based on their surface image. We trained and tested the developed CNN using a database of 5000 potato images. We compared the results of potato defect classification based on CNN with the traditional neural network and Support Vector Machine (SVM). The results show that the accuracy of the deep learning method is higher than the Traditional Method. We get 100% and 99% accuracy in some of the classes, respectively.

scholarly journals Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study (Preprint)

2019 ◽  
Author(s):  
Po-Ting Lai ◽  
Wei-Liang Lu ◽  
Ting-Rung Kuo ◽  
Chia-Ru Chung ◽  
Jen-Chieh Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Disease Association ◽  
Support Vector ◽  
Context Aware ◽  
Neural Network Architecture ◽  
Large Margin

BACKGROUND Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. OBJECTIVE As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. METHODS In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. RESULTS Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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