Fault diagnosis for rolling bearing based on VMD-FRFT

Li, Xin; Ma, Zengqiang; Kang, De; Li, Xiang

doi:10.1016/j.measurement.2020.107554

Cited by 76 publications

(47 citation statements)

References 24 publications

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“…The modern development of sensing, measurement technology, and computer-based fault diagnosis technology are developing increasingly prosperous [15][16][17][18][19]. With the in-depth research of many scholars and experts, many emerging fault diagnosis and analysis techniques have been continuously applied to practical engineering [10,20]. As large-scale and large-capacity mechanical equipment, wind turbines have a relatively complete fault diagnosis system.…”

Section: Rolling Bearing Vibration Mechanism and Characteristic Signal Frequencymentioning

confidence: 99%

“…Literature [9] uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Linearly Decreasing Particle Swarm Optimization Probabilistic Neural Network (LDWPSO-PNN) methods to analyze and compare the vibration signals of rotating machinery. Literature [10] uses the Variational Modal Decomposition Fractional Fourier Transform (VMD-FRFT) method to diagnose rolling bearing faults. This paper takes the rolling bearings as the research object and analyzes the states of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

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Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Meng¹,

Wang²,

Yang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

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Section: Rolling Bearing Vibration Mechanism and Characteristic Signal Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Meng¹,

Wang²,

Yang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…It has an adaptive time-frequency-scale-frequency modulation-rate-conversion window that can reflect any local details of the signal. The STFRFT algorithm is widely used in the IF extraction of a rolling bearing [40][41][42] and some fractional models [43][44][45]. In reference [46], considering the characteristics of a rolling bearing signal, the fractional Fourier transform (FRFT) is introduced into VMD to realize the adaptive decomposition of VMD and improve the noise robustness of the algorithm.…”

Section: B Related Research Of the Stfrftmentioning

confidence: 99%

“…The STFRFT algorithm is widely used in the IF extraction of a rolling bearing [40][41][42] and some fractional models [43][44][45]. In reference [46], considering the characteristics of a rolling bearing signal, the fractional Fourier transform (FRFT) is introduced into VMD to realize the adaptive decomposition of VMD and improve the noise robustness of the algorithm. In [47], ensemble empirical mode decomposition based on a fractional Fourier transform was proposed to detect and estimate the parameters of multicomponent chirp signals, but with the limitations of ensemble empirical mode decomposition, the modal aliasing problem is not solved well.…”

Section: B Related Research Of the Stfrftmentioning

confidence: 99%

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

Liu

et al. 2020

IEEE Access

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The time-frequency energy distribution processed by a short-time Fourier transform can be compressed to the real instantaneous frequency by the synchrosqueezing transformation (SST), which improves the time-frequency energy concentration of the signal. However, there is a large error in the instantaneous frequency estimation of a multicomponent nonpure harmonic signal by the SST. Therefore, a method for determining the instantaneous frequency (IF) of a rolling bearing based on a fractional synchrosqueezing transformation (FRSST) is proposed. First, the theoretical derivation of the FRSST algorithm as a signal processing technique is given and the steps of the IF estimation are presented. Second, the main advantages of the proposed FRSST algorithm are proved. In the comparison of simulation signals, it is verified that the FRSST algorithm has a high time-frequency concentration, is non-fragile to the frequency modulation rate, has noise robustness and has nonsensitivity to the cross-frequency signal. Finally, the FRSST algorithm is applied to the IF estimation of a rolling bearing under rising speed and fluctuated speed, and is compared with the SST based on variational mode decomposition (VMD-SST), the generalized parametric SST (PSST) and polynomial chirplet transform (PCT). The test results show that the estimation error of IF based on the FRSST method is the least for a rolling bearing with the four fault types under rising speed. On average, the estimation error is 2.2180 Hz less than the corresponding error of the VMD-SST and 1.1862 Hz less than the corresponding error of the PSST method. INDEX TERMS Fractional synchrosqueezing transformation, instantaneous frequency, noise robustness, rolling bearing.

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“…It has better efficacy in extracting fault features and has been used in fault diagnosis of machinery. 41–45…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

Patil

Mishra

Harsha

2021

Proceedings of the Institution of Mechanical Engineers, Part K:

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Maintenance planning plays a critical role in the process industry, where any unplanned maintenance may lead to a significant loss. Condition monitoring happens to aid maintenance planning and has become an inherent part of the maintenance activity. Physical parameters such as vibration, acoustic emission, current, etc., are used for condition monitoring, out of which vibration is the most preferred parameter and is widely used in the industry. Vibration data is measured near to bearings, which themselves are monitored for their condition, and hence rolling element bearing (REB) is the focus of this study. REBs are monitored for the presence of a fault in them as well as for their severity. Fault diagnosis of REB using harmonic product spectrum (HPS) is proposed in this study. The proposed methodology's novelty lies in the signal pre-processing step, whose output is fed to the HPS method, which is used for defective raceway identification. The efficacy of HPS is assessed with parameter optimized Variational mode decomposition (VMD) and classical bandpass filtering method as pre-processors. It is observed that the HPS delivers better diagnostic results with the VMD method than the bandpass filtering method. Non-dominated sorting particle swarm optimization algorithm is deployed for parameter optimization of VMD. HPS combined with VMD as pre-processor forms an autonomous HPS(AHPS) algorithm, whose input is measured signal and output is defect frequency. The process is so designed that a raw signal, when fed to the algorithm, delivers the result as identification of a defective raceway. Unlike previously developed methods, the proposed method needs no manual intervention. Results obtained from simulated signals and signals recorded through experiments validate that the proposed methodology can be used effectively for fault diagnosis of REB.

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Fault diagnosis for rolling bearing based on VMD-FRFT

Cited by 76 publications

References 24 publications

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

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