scholarly journals A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1096
Author(s):  
Chenggong Zhang ◽  
Daren Zha ◽  
Lei Wang ◽  
Nan Mu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Second Order ◽  
Fault Classification ◽  
Global Average ◽  
First Order ◽  
Difference Sequences ◽  
Soft Fault ◽  
Soft Fault Diagnosis

This paper develops a novel soft fault diagnosis approach for analog circuits. The proposed method employs the backward difference strategy to process the data, and a novel variant of convolutional neural network, i.e., convolutional neural network with global average pooling (CNN-GAP) is taken for feature extraction and fault classification. Specifically, the measured raw domain response signals are firstly processed by the backward difference strategy and the first-order and the second-order backward difference sequences are generated, which contain the signal variation and the rate of variation characteristics. Then, based on the one-dimensional convolutional neural network, the CNN-GAP is developed by introducing the global average pooling technical. Since global average pooling calculates each input vector’s mean value, the designed CNN-GAP could deal with different lengths of input signals and be applied to diagnose different circuits. Additionally, the first-order and the second-order backward difference sequences along with the raw domain response signals are directly fed into the CNN-GAP, in which the convolutional layers automatically extract and fuse multi-scale features. Finally, fault classification is performed by the fully connected layer of the CNN-GAP. The effectiveness of our proposal is verified by two benchmark circuits under symmetric and asymmetric fault conditions. Experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

scholarly journals Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 972 ◽  
Author(s):  
Xingchen Liu ◽  
Qicai Zhou ◽  
Jiong Zhao ◽  
Hehong Shen ◽  
Xiaolei Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Industrial Applications ◽  
Experimental Result ◽  
Noisy Environment ◽  
Experimental Conditions ◽  
Global Average ◽  
Noisy Input ◽  
Convolutional Autoencoder

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

Adaptive BP Wavelet Neural Network Method for Soft Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 307 ◽  
pp. 327-330
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Wavelet Neural Network ◽  
Learning Ability ◽  
Time Frequency ◽  
Soft Fault ◽  
Time Frequency Localization ◽  
Soft Fault Diagnosis

Because of the diversity and complexity of soft fault in analog circuit, the rapid and accurate diagnosis is very difficult. For this, an adaptive BP wavelet neural network diagnosis method of soft fault is proposed. It combines the time-frequency localization characteristics of wavelet and the self-learning ability of neural network in soft fault diagnosis of analog circuit, and by introducing the adaptive learning rate the diagnosis ability of BP wavelet neural network model can effectively be improved. In addition, PSPICE software is used to obtain the simulation data of actual analog circuit for the experiment. The results also verify the validity of the proposed method.

scholarly journals Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network

2021 ◽  
Vol 67 (10) ◽  
pp. 489-500
Author(s):  
Shuai Yang ◽  
◽  
Xing Luo ◽  
Chuan Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Industrial Robot ◽  
Rotation Vector ◽  
Fault Classification ◽  
Economic Losses ◽  
Network Training ◽  
Rv Reducer

As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.

scholarly journals Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Qian
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Network Models ◽  
Fault Classification ◽  
Neural Network Models ◽  
Diagnosis Algorithm ◽  
Diagnosis Model ◽  
Network Fault

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network

2013 ◽  
Vol 712-715 ◽  
pp. 1965-1969 ◽  
Author(s):  
Bao Ru Han ◽  
Jing Bing Li ◽  
Heng Yu Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Fitness Function ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Soft Fault ◽  
Soft Fault Diagnosis

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

scholarly journals Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 770 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Zuoyi Chen ◽  
Xuebing Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

scholarly journals A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1693 ◽  
Author(s):  
Gong ◽  
Chen ◽  
Zhang ◽  
Zhang ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

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