Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network

Wu, You; Zhao, Rongzhen; Jin, Wuyin; He, Tianjing; Ma, Sencai; Shi, Mingkuan

doi:10.1007/s10489-020-02006-6

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…To further confirm the LGSHE algorithm's DR effect, the low-dimensional feature set obtained by LGSHE was input to three different classifiers, KNN [33], SVM [34], and BP [35], and figure 12 shows the final results. As can be observed from figure 12, LGSHE shows good results on different classifiers, indicating that it has high DR capabilities.…”

Section: Other Performance Analysis Of Lgshementioning

confidence: 99%

Dimensionality reduction using local-global standard hypergraph embedding for rotor fault diagnosis

Yuan¹,

Zhao²,

Chen³

et al. 2022

Meas. Sci. Technol.

Self Cite

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The vibration signal contains a lot of state information, and its time domain, frequency domain, and time-frequency domain features are extracted to describe the rotor’s operational state in detail. However, multi-domain and multi-angle feature extraction can lead to information redundancy, causing the "dimensionality catastrophe" problem, which also makes fault classification more difficult. Dimensionality reduction (DR) is a technique that can help with this issue, but the majority of current DR algorithms are based on a simple intrinsic structure. To accurately characterize the intrinsic structure of high-dimensional fault dataset, this study proposed a novel DR algorithm that considers local information, global information, and the hypergraph model, named Local-Global Standard Hypergraph Embedding (LGSHE). LGSHE redefines the hypergraph weight matrix’s calculation formula and constructs the local intra-class standard hypergraph, local inter-class standard hypergraph, global intra-class standard hypergraph, and global inter-class standard hypergraph to characterize the fault data structure. LGSHE can accurately characterize the intrinsic structure of high-dimensional fault dataset and increase fault classification accuracy. The performance of LGSHE is validated on two different structures of double-span rotor experimental benches, and the results show that the algorithm can achieve good fault classification accuracy.

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Section: Other Performance Analysis Of Lgshementioning

confidence: 99%

Dimensionality reduction using local-global standard hypergraph embedding for rotor fault diagnosis

Yuan¹,

Zhao²,

Chen³

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…4,5 The increasing complexity of the working environment, long-term operation, and environmental factors will cause specific damage to machinery and equipment, resulting in reduced industrial production efficiency, economic losses, and even casualties. 6,7 Due to the advantages of intelligent fault diagnosis (IFD) methods in identifying the health status of machines, a lot of research has been done on them.…”

Section: Introductionmentioning

confidence: 99%

“…The 2-D CNN architecture was used to fuse the data acquired by current sensors to achieve the effective classification of gear faults without manual feature extraction (FE). Shao et al 31 designed an adaptive DBN combined with a wavelet packet variant of the rolling bearing fault diagnosis method, taking into account the ability of signal processing and adaptive FE; Wu et al 7 proposed a semi-supervised CNN applied to rolling bearing faults. The relationship between class spacing and intra-class distance in spatial features was embedded in the CNN framework to achieve rolling bearing fault identification.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples

Sun

Wang

Sun

et al. 2022

Structural Health Monitoring

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Currently, the monitoring of the health status of mechanical systems is becoming more and more critical, and the actual monitoring data is massive and high-dimensional, and these data are characterized mainly by imbalance. To solve the problem of low fault recognition rate caused by data imbalance, this article proposed a novel local and non-local information balanced neighborhood graph embedding interpretable deep autoencoder (LGBNGEDAE) method for rotating machinery fault diagnosis. Specifically, the local and non-local neighborhood information graph is embedded into the original objective function of the deep autoencoder to smooth the manifold structure of the data in LGBNEDSAE, which endows the deep learning model with better data learning capability and robustness of feature extraction as well as more reasonable network interpretability, making it more suitable for feature learning and classification of unbalanced data. Further, the method’s excellent fault diagnosis capability and generalization performance are verified on two opposed experimental cases of gearboxes. Compared with other deep learning architectures and shallow learning models, the method performs better in the face of unbalanced datasets.

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“…Experiments show that our semisupervised convolutional neural network can train the model with unlabeled samples and improve the performance of diagnosis. Its advantages are obvious compared with common methods [8]. With the development of society and the improvement of living standards, people have higher and higher requirements for air quality in building rooms.…”

Section: Introductionmentioning

confidence: 99%

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

Yi¹

2021

Mobile Information Systems

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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.

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Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network

Cited by 22 publications

References 23 publications

Dimensionality reduction using local-global standard hypergraph embedding for rotor fault diagnosis

Dimensionality reduction using local-global standard hypergraph embedding for rotor fault diagnosis

Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples

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

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