scholarly journals Research on typical fault classification method of DC-DC converter

2021 ◽  
Vol 39 (1) ◽  
pp. 71-76
Author(s):  
Zhao Xu ◽  
Yitong Zhang ◽  
Zhen Pan ◽  
Chengzhi Chi ◽  
Xiaobin Liu
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Original Data ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Core Component ◽  
Feature Extraction And Selection ◽  
Use Efficiency ◽  
Integrated Modular Avionics

DC-DC converter is the core component of power conversion module of integrated modular avionics. Condition monitoring and fault diagnosis of DC-DC converter can effectively improve the reliability of avionics equipment, reduce the maintenance cost and greatly improve the use efficiency of aircraft. In this paper, firstly, a typical DC-DC converter model based on SEPIC topology is designed in PSPICE environment, and the failure modes of DC-DC converter are analyzed. Secondly, the typical fault types of DC-DC converter are simulated, and the corresponding original data are obtained through simulation. Finally, the processing framework including data preprocessing, feature extraction and selection, and multi model fusion is used to do fault classification of the DC-DC converter. The fault diagnosis of the converter is simulated. Simulation results show the effectiveness of the proposed method.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Language Processing ◽  
Classification Performance ◽  
Fault Classification ◽  
Shaft Speed ◽  
Complex Envelope ◽  
The Past ◽  
Feature Extraction And Selection ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE-) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

scholarly journals The Method of Communication System Fault Diagnosis Based on Deep Belief Net

2021 ◽  
Vol 15 ◽  
pp. 978-985
Author(s):  
Juan Li ◽  
Bin Chen
Keyword(s):  
Fault Diagnosis ◽  
Communication System ◽  
Health Management ◽  
Bottom Layer ◽  
Original Data ◽  
Fault Classification ◽  
Deep Belief Net ◽  
Communication Experiment ◽  
And Training ◽  
Communication Equipment

To meet the need of fault diagnosis for military communication system, an effective method based on deep belief (DBN) net is proposed. During the fault diagnosis, the bottom layer of DBN model is used to receive the input fault signals to extract the fault features and the fault classification results will be outputted after softmax classified. Accordingly, algorithms for DBN model and training and RBM parameter learning have been designed. To reduce the running time, parallel solutions based on MapReduce framework have been provided. In order to test and verify the effect of DBN fault diagnosis, the communication experiment system is built in the laboratory which the output signals of the transmitter and the receiver are measured and collected as the original data for further learning and training. Compared with the traditional fault diagnosis methods, it can be found that DBN method has high accuracy in fault diagnosis and the process is simple and friendly. It is impossible to realize real-time diagnosis and online diagnosis for the communication system. The research can be applicated to the health management of communication equipment, and it will provide advanced technical support and software program for the health of communication equipment

scholarly journals Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm

2019 ◽  
Vol 16 (23) ◽  
pp. 4868 ◽  
Author(s):  
Zhongxin Chen ◽  
Feng Zhao ◽  
Jun Zhou ◽  
Panling Huang ◽  
Xutao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Sound Intensity ◽  
Original Data ◽  
National Standards ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Matlab Simulation ◽  
Independent Components

When a part of the loader’s gearbox fails, this can lead to equipment failure due to the complex internal structure and the interrelationship between the parts. Therefore, it is imperative to research an efficient strategy for transmission fault diagnosis. In this study, the non-contact characteristics of noise diagnosis using sound intensity probes were used to collect noise signals generated under gear breaking conditions. The independent component analysis (ICA) technique was applied for feature extraction from the original data and to reduce the correlation between the signals. The correlation coefficient between the independent components and the source data was used as the input parameters of the support vector machine (SVM) classifier. The separation of the independent components was achieved by MATLAB simulation. The misdiagnosis rate was 5% for 40 sets of test data. A 13-point test platform for noise testing of the loader gearbox was built according to Chinese national standards. Source signals under the normal and fault conditions were analyzed separately by ICA and SVM algorithms. In this case, the misdiagnosis rate was 7.5% for the 40 sets of experimental test data. This proved that the proposed method could effectively realize fault classification and recognition.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Ma ◽  
Shitong Liang ◽  
Zhengyu Du ◽  
Ming Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Training Set ◽  
Sample Data ◽  
Diagnosis Method ◽  
Main Fault ◽  
Bp Model ◽  
Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding

2014 ◽  
Vol 1008-1009 ◽  
pp. 983-987
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

scholarly journals Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Yuan Gao ◽  
Kai Lin ◽  
Ye Hu
Keyword(s):  
Fault Diagnosis ◽  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Fine Grained ◽  
Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

scholarly journals Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baokun Han ◽  
Sixiang Jia ◽  
Guifang Liu ◽  
Jinrui Wang
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Extraction Process ◽  
Fault Classification ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Excellent Performance ◽  
Adversarial Networks ◽  
And Training

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.

Analyzing the Russian tool-making industry within the sustainable development concept

2020 ◽  
Vol 16 (4) ◽  
pp. 662-677 ◽  
Author(s):  
T.N. Ryzhikova ◽  
V.G. Borovskii
Keyword(s):  
Sustainable Development ◽  
Hierarchical Level ◽  
Economic Systems ◽  
Resource Use Efficiency ◽  
The Sustainable Development ◽  
Case Methods ◽  
Hierarchical Levels ◽  
Use Efficiency ◽  
Intensive Development

Subject. The article explores the economic component of sustainable development through the classification of industries from the perspective of assigning the output goods to fixed or current assets, using different types of resources and assessing their condition. It considers factors of production affecting the achievement of the result and determining the development of economic systems at the meso- and micro-hierarchical level. Objectives. The aim is to assess the function of economic systems of different hierarchical levels on the tool-making facilities case. Methods. We apply an approach using general and special methods of comparative and statistical analysis, synthesis and analogies. Results. The paper analyzes the development of the Russian tool-making industry on the basis of resource-oriented approach to assess the functioning of economic systems at various levels. We review interrelations between resource use efficiency, types of sustainable development of economic systems at various hierarchical levels and strategic security of the country. Conclusions. Any development can be considered sustainable, if there is an increment in the intensive development of economic systems at all three levels.

Classification of potato cultivars by their nitrogen use efficiency

2019 ◽  
Vol 45 (1) ◽  
pp. 153
Author(s):  
Dan-Dan HE ◽  
Li-Guo JIA ◽  
Yong-Lin QIN ◽  
Ming-Shou FAN
Keyword(s):  
Nitrogen Use Efficiency ◽  
Potato Cultivars ◽  
Nitrogen Use ◽  
Use Efficiency
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