Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

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Application of ART-2 Neural Network and Invariant Moment in Image Pattern Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4014 ◽

2012 ◽

Vol 433-440 ◽

pp. 4014-4019 ◽

Cited By ~ 22

Author(s):

Lei Hao ◽

Yue Hua Gao ◽

Rui Jun Jia

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Feature Extraction ◽

Recognition Rate ◽

Experimental Results ◽

Invariant Moment ◽

Recognition Model ◽

Image Pattern

This paper mainly uses image pre-processing and feature extraction to calculate the invariant moment of image, and ultimately realizes the image pattern recognition based on ART-2 neural network. Experimental results show that ART-2 neural network has high recognition rate. It also solves the contradiction between network's plasticity and stability, when new recognition model appears.

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Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3804 ◽

2013 ◽

Vol 756-759 ◽

pp. 3804-3808

Author(s):

Zhi Mei Duan ◽

Jia Tang Cheng

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Power Transformer ◽

Recognition Rate ◽

Particle Swarm ◽

Adaptive Mutation ◽

Bp Network ◽

Swarm Optimization ◽

Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

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Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.459 ◽

2014 ◽

Vol 608-609 ◽

pp. 459-467 ◽

Cited By ~ 1

Author(s):

Xiao Yu Gu

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Decision Theory ◽

Recognition Rate ◽

Recognition Algorithm ◽

Modulation Recognition ◽

The Core ◽

Feature Parameters ◽

Communication Signal ◽

Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

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Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.2042 ◽

2011 ◽

Vol 189-193 ◽

pp. 2042-2045 ◽

Cited By ~ 1

Author(s):

Shang Jen Chuang ◽

Chiung Hsing Chen ◽

Chien Chih Kao ◽

Fang Tsung Liu

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Gaussian Distribution ◽

Test Pattern ◽

Recognition Rate ◽

Hopfield Neural Network ◽

Training Data ◽

New Method ◽

Gaussian Filter ◽

Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

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Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Engineering Research Express ◽

10.1088/2631-8695/ac4834 ◽

2022 ◽

Author(s):

Canyi Du ◽

Rui Zhong ◽

Yishen Zhuo ◽

Xinyu Zhang ◽

Feifei Yu ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Pattern Recognition Method ◽

Recognition Method ◽

Vibration Signals ◽

One Dimensional ◽

Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/16 ◽

2020 ◽

Vol 17 (4) ◽

pp. 572-578

Author(s):

Mohammad Parseh ◽

Mohammad Rahmanimanesh ◽

Parviz Keshavarzi

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Support Vector ◽

Svm Classifier ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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Pattern Recognition by Hierarchical Feature Extraction

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2003.p0278 ◽

2003 ◽

Vol 15 (3) ◽

pp. 278-285

Author(s):

Daigo Misaki ◽

◽

Shigeru Aomura ◽

Noriyuki Aoyama

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Feature Extraction ◽

Input Data ◽

Local Features ◽

Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

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