Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model

Wang, Hongchao; Guo, Zhiqiang; Du, Wenliao

doi:10.1007/s12206-021-0705-y

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…The most commonly used method is detrended fluctuation analysis (MF-DFA). Papers [ 44 , 45 , 46 ] describe its application for diagnosing damage to rolling bearings and, in [ 47 , 48 , 49 ], for detecting errors in toothed gears. An adaptive version of this method is presented in [ 50 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Komorska

Olejarczyk

Puchalski

et al. 2023

Sensors

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The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Komorska

Olejarczyk

Puchalski

et al. 2023

Sensors

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“…Many scholars have conducted a lot of research on extracting time-domain features. Wang et al [3] proposed a new method for predicting the fault state of rolling bearings by monitoring the vibration frequency and amplitude of the bearing seat. The method takes the kurtosis of the calculated vibration signal as the fault feature, and the kurtosis of the undamaged rolling bearing remains unchanged.…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Fault Detection Method of Rolling Bearing Based on Signal Feature Fusion Technology

Fang,

Wu,

Wang

et al. 2023

Applied Sciences

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Rolling bearings are the core transmission components of large-scale equipment. Once a fault occurs, the consequences may be catastrophic, posing a serious threat to life and the safety of the property. Aimed at the problem of rolling bearing faults, this paper analyzes the characteristics of different fault signals and proposes a fault diagnosis method based on fuzzy signals. Based on the definition of an incomplete mapping of a new connotation, this paper proposes a fuzzy fault diagnosis method by fuzzy mapping, gives a fuzzy signal processing algorithm, and discusses two judgment principles. Further, it carries out a vibration signal analysis of the rolling bearing. According to the fault diagnosis method in this paper, the fault rolling bearing is diagnosed. The experimental results show that the proposed method can effectively diagnose rolling bearing faults closer to their natural attributes and solves the problem of traditional generator bearing fault diagnosis that requires complex models and poor diagnosis speed. Further, it can be seen that the average time consumption of this method is reduced and the fault recognition accuracy rate is increased. Compared with the existing related methods, this proposed diagnosis method is superior to that of several existing methods. It not only has higher precision, stronger anti-noise capacity, and faster diagnosis speed, but also has lower effective information loss.

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“…Detrended fluctuation analysis MF-DFA is a commonly used algorithm for multifractal analysis. The method is often used to diagnose damage to rolling bearings [31][32][33] and gears [34][35][36]. In [37], the MF-DFA algorithm was used for the analysis of frictional vibrations, where the ensemble empirical mode decomposition EEMD was used to denoise the signal.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Komorska

Puchalski

2021

Sensors

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Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

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Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model

Cited by 8 publications

References 37 publications

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Research on Improved Fault Detection Method of Rolling Bearing Based on Signal Feature Fusion Technology

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

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