Research on the health status evaluation method of rolling bearing based on EMD‐GA‐BP

Liu, Yangshuo; Kang, Jianshe; Bai, Yunjie; Guo, Chiming

doi:10.1002/qre.3350

Cited by 3 publications

(1 citation statement)

References 7 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of optimal demodulation frequency band (ODFB), Wang et al [6] proposed an ergodic index enhanced graph (TIEgram), which uses the ergodic segmentation model to transfer the original signal into the frequency domain to solve the problem that a single indicator cannot detect different signals. In recent years, empirical mode decomposition (EMD) has been widely applied in fault feature extraction [7]. Aggarwal et al [8] used EMD to process current signals and carried out feature extraction for 10 kinds of shunt faults.…”

Section: Introductionmentioning

confidence: 99%

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Zhang,

Zhou,

et al. 2024

Processes

View full text Add to dashboard Cite

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the support vector machine (SVM) for fault diagnosis and classification, and the parameter optimization ability of SSA is used again to build the SSA-SVM fault diagnosis model. To evaluate the running state of bearings, an alarm threshold method based on the root mean square value calculated by cosine similarity and 3σ is proposed to divide samples of different health states. Finally, the method constructed in this paper is compared with other methods by using simulation and experimental data sets, and the running condition monitoring and fault diagnosis of rolling bearings are successfully realized, which shows the superiority and effectiveness of the method proposed in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Zhang,

Zhou,

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

Research on the Health State Assessment of Electromagnetic Relays Based on PSO-SVM-DS

Hu,

Wei,

et al. 2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals

Wang,

Zheng,

Song

et al. 2024

Machines

View full text Add to dashboard Cite

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods.

show abstract

Research on the health status evaluation method of rolling bearing based on EMD‐GA‐BP

Cited by 3 publications

References 7 publications

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Research on the Health State Assessment of Electromagnetic Relays Based on PSO-SVM-DS

A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals

Contact Info

Product

Resources

About