An Adaptive Optimized TVF-EMD Based on a Sparsity-Impact Measure Index for Bearing Incipient Fault Diagnosis

Ye, Xuerong; Hu, Y.; Shen, Jun‐Xian; Chen, Cen; Zhai, Guofu

doi:10.1109/tim.2020.3044517

Cited by 38 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where x represents the value of the evaluation result entered by the affiliation function and y is the probability value of affiliation to the normal state. It is noted that the affiliation function of the state is shown in Equation (5).…”

Section: Assessment Classification Based On Fuzzy Theorymentioning

confidence: 99%

See 1 more Smart Citation

Converter valve state evaluation method based on hierarchical graded fuzzy theory

Yang,

Li,

Liu

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

As the core equipment of the DC power transmission project, the operation status of the converter valve is the key to the safety and stability of the DC system. Existing assessment methods cannot accurately assess the health status of the converter valve in project operation. According to the independence of the function and structure of each component of the converter valve, this paper proposes a converter valve state evaluation method based on hierarchical grading fuzzy theory. The data used are various information characterizing the state of the converter valve, such as voltage, current, sound, image, and other data. According to hierarchical grading, the evaluation index system is constructed. Based on the fuzzy theory, the affiliation function is proposed, and the affiliation probability of four hierarchical states is output for the state evaluation results of the converter valve and its group components. Experiments show that the method improves the accuracy and practicability of converter valve evaluation in engineering sites.

show abstract

Section: Assessment Classification Based On Fuzzy Theorymentioning

confidence: 99%

“…It is mainly divided into two categories. One is the traditional signal feature extraction processing methods, for example, empirical modal decomposition [3,4], Fourier transforms [5], etc. The characteristic quantity of the fault is extracted through the signal processing method, and then the fault is classified.…”

Section: Introductionmentioning

confidence: 99%

Converter valve state evaluation method based on hierarchical graded fuzzy theory

Yang,

Li,

Liu

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Traditional signal processing knowledge combined with machine learning methods, such as the BP neural network and Empirical Mode Decomposition (EMD), can effectively identify fault types and predict the operation status of components [ 4 , 5 , 6 , 7 , 8 , 9 ]. However, the feature extraction process of these methods seriously relies on professional knowledge and can easily consume many resources in the era of big data [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Wang

Tong

et al. 2023

Sensors

View full text Add to dashboard Cite

The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.

show abstract

“…One of the most effective and practical methods is signal decomposition, including wavelet packet decomposition [ 6 ], empirical mode decomposition (EMD) [ 7 , 8 ], local mean decomposition (LMD) [ 9 , 10 ], empirical wavelet transform (EWT) [ 11 , 12 , 13 , 14 ], variational mode decomposition (VMD) [ 15 , 16 , 17 , 18 ], and so on. Signal decomposition can extract the useful component containing fault characteristic information and thus achieve the purpose of removing noise and interference components.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

View full text Add to dashboard Cite

Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

show abstract

An Adaptive Optimized TVF-EMD Based on a Sparsity-Impact Measure Index for Bearing Incipient Fault Diagnosis

Cited by 38 publications

References 42 publications

Converter valve state evaluation method based on hierarchical graded fuzzy theory

Converter valve state evaluation method based on hierarchical graded fuzzy theory

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Contact Info

Product

Resources

About