scholarly journals Feedforward Chaotic Neural Network Model for Rotor Rub-Impact Fault Recognition Using Acoustic Emission Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Wei Peng ◽  
Weidong Liu ◽  
Xinmin Cheng ◽  
Liping Shi
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Network Models ◽  
Normal Operation ◽  
Maximum Efficiency ◽  
Chaotic Neural Network ◽  
Bp Network ◽  
Fault Recognition

The rubbing faults caused by dynamic and static components in large rotatory machine are dangerous in manufacture process. This paper applies a feedforward chaotic neural network (FCNN) to recognize acoustic emission (AE) source in rotor rubbing and diagnose the rotor operational condition. This method adds the dynamic chaotic neurons based on logistic mapping into the multilayer perceptron (MLP) model to avoid the network falling into a local minimum, the delayed and feedback structure for maximum efficiency of recognition performance. The AE data was rotor rubbing process sampled from the test rig of rotatory machine, classification by fault degree. The experimental results indicate that the recognition rate is superior to the traditional BP network models. It is an effective method to recognize the rubbing faults for the machine normal operation.

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

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.

scholarly journals Partial discharge detection for stator winding insulation of motors using artificial neural network

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878612 ◽  
Author(s):  
Yu-Tung Chen ◽  
Jui-Chien Lai ◽  
Yu-Ming Jheng ◽  
Cheng-Chien Kuo ◽  
Hong-Chan Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Voltage ◽  
Partial Discharge ◽  
Recognition Rate ◽  
Gradient Algorithm ◽  
Normal Operation ◽  
Test Models ◽  
Artificial Neural ◽  
Stator Fault

In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network–based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.

Fault Detection of Carbide Anvil Based on Hurst Exponent and BP Neural Network

2013 ◽  
Vol 805-806 ◽  
pp. 1881-1886 ◽  
Author(s):  
Li Han ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Zhao Li Yan ◽  
Xiao Bin Cheng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Hurst Exponent ◽  
Recognition Rate ◽  
Pulse Signal ◽  
Characteristic Parameters ◽  
Threshold Method ◽  
Fault Recognition ◽  
Bp Neural Network Model

This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.

Application BP Neural Network in the Speaker Recognition Based on Chaos Particle Swarm Optimization Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2805-2808
Author(s):  
Guo Wen Wang ◽  
Shi Xin Luo ◽  
Li He ◽  
Gang Yin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Speaker Recognition ◽  
Particle Swarm Optimization Algorithm ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Bp Network ◽  
Swarm Optimization

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.

scholarly journals Fault Detection of Reciprocating Compressor Valve Based on One-Dimensional Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fu-yan Guo ◽  
Yan-chao Zhang ◽  
Yue Wang ◽  
Ping Wang ◽  
Pei-jun Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Oil And Gas ◽  
Recognition Rate ◽  
Reciprocating Compressor ◽  
One Dimensional ◽  
Healthy Development ◽  
Fault Recognition ◽  
Diagnosis Model ◽  
Artificial Neural Network Ann

Reciprocating compressors are important equipment in oil and gas industries which closely relate with the healthy development of the enterprise. It is essential to detect the valve fault because valve failures account for 60% in total failures. For this field, an artificial neural network (ANN) is widely used, but a complex network is not suitable for its low accuracy and easy overfitting. This paper proposes a fault diagnosis model of a reciprocating compressor valve based on a one-dimensional convolutional neural network (1DCNN). This method takes the differential pressure and differential temperature of each compressor stage as the input of 1DCNN, using the characteristics of the CNN to extract the features and finally using Softmax to classify the fault. In order to verify this method, it is compared with LM-BP, RBF, and BP neural networks. The results show that the fault recognition rate of 1DCNN reaches 100%, which proves the effectiveness and feasibility of the proposed method.

Concrete Damage Identification based on Acoustic Emission and Wavelet Neural Network

2022 ◽  
Vol 80 (1) ◽  
pp. 48-57
Author(s):  
Yan Wang ◽  
Lijun Chen ◽  
Na Wang ◽  
Jie Gu
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Source Identification ◽  
Basic Parameter ◽  
Damage Identification ◽  
Recognition Rate ◽  
Principal Component ◽  
Wavelet Neural Network ◽  
Tensile Data ◽  
Concrete Damage

In order to improve the accuracy of damage source identification in concrete based on acoustic emission testing (AE) and neural networks, and locating and repairing the damage in a practical roller compacted concrete (RCC) dam, a multilevel AE processing platform based on wavelet energy spectrum analysis, principal component analysis (PCA), and a neural network is proposed. Two data sets of 15 basic AE parameters and 23 AE parameters added on the basis of the 15 basic AE parameters were selected as the input vectors of a basic parameter neural network and a wavelet neural network, respectively. Taking the measured tensile data of an RCC prism sample as an example, the results show that compared with the basic parameter neural network, the wavelet neural network achieves a higher accuracy and faster damage source identification, with an average recognition rate of 8.2% and training speed of about 33%.

Recognition of partial discharge of cable accessories based on convolutional neural network with small data set

2020 ◽  
Vol 39 (2) ◽  
pp. 431-446
Author(s):  
Anan Zhang ◽  
Jiahui He ◽  
Yu Lin ◽  
Qian Li ◽  
Wei Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Measure Data ◽  
Small Data ◽  
Data Set ◽  
Content Type ◽  
Fault Recognition ◽  
Small Data Set

Purpose Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN). Design/methodology/approach Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition. Findings The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN. Originality/value In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

Sign in / Sign up

Export Citation Format

Share Document