Quantitative diagnosis of fault severity trend of rolling element bearings

Cui, Lingli; Ma, Chunqing; Zhang, Feibin; Wang, Huaqing

doi:10.3901/cjme.2015.0715.094

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…Algorithm 1. Sparse time-frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) 1) , Maxiter = 100 1:…”

Section: Solution Of the Model Based On The First-order Primal-dual Amentioning

confidence: 99%

“…Rolling bearings are important and vulnerable components widely used in rotating machinery. The running state of rolling bearings significantly affects the performance, safety, and reliability of the overall machinery [1][2][3]. Therefore, the state detection and fault diagnosis for rolling bearings have 2 of 23 important practical significance.…”

Section: Introductionmentioning

confidence: 99%

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Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Chen

Meng

et al. 2018

Applied Sciences

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Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.

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“…Algorithm 1. Sparse time-frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) 1) , Maxiter = 100 1:…”

Section: Solution Of the Model Based On The First-order Primal-dual Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Chen

Meng

et al. 2018

Applied Sciences

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“…It is the component that supports the rotating shaft and has a poor ability to withstand impact loads. According to statistics, about 30% of failures in rotating equipment are caused by damage to rolling bearings [1] . These failures can lead to sudden damage of the equipment, causing economic losses and even threatening the personal safety of workers, seriously affecting production safety and efficiency [2] .…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on convolutional neural network and transfer learning

Pan,

Xiang

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…Rotating machinery, e.g., rotors, bearings, and gears, has a close correlation with characteristic frequencies, such as rotation frequency, passing frequency, and meshing frequency, in their dynamic characteristics under failure. Therefore, the focus of related research was on effectively extracting the frequency domain characteristics of early weak faults from the signals of non-stationary conditions [6]- [8]. The work of reciprocating mechanical parts was periodic.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Zhao

Zhang

Mao

et al. 2021

Preprint

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Reciprocating machinery, e.g., diesel engines and reciprocating compressors, is the key power component in petroleum, petrochemical, nuclear power, and shipbuilding industries. Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery; therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. Moreover, failures occur frequently every year, causing serious economic losses. To accurately and efficiently extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals was conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number K and second penalty factor was proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) was established. A search strategy for wavelet local parameters of multi-impact signals was proposed using the harmony search (HS) method. A multi-source shock simulation signal was established and actual data of the valve fault were obtained through diesel engine fault experiments. The test results demonstrated that the new method achieved adaptive extraction of local shock features of non-stationary multi-source shock signals and was superior to the original method in terms of signal decomposition effect, sensitive feature extraction, fault recognition accuracy, and parameter search time. The fault recognition rate of the intake and exhaust valve clearance was above 90% and the extraction accuracy of the shock start position was improved by 10°.

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Quantitative diagnosis of fault severity trend of rolling element bearings

Cited by 12 publications

References 17 publications

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Fault diagnosis of rolling bearing based on convolutional neural network and transfer learning

Time-Frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

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