Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

Zhao, Dongfeng; Li, Jianyong; Chen, Weidong

doi:10.1155/2015/425989

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…For a different operating speed, a new instance of the classifier needs to be retrained on a new set of features. In [40], a mechanism for feature extraction was proposed, which can be used to diagnose bearing faults under gear interference and variable speed conditions. However, this approach is very tedious and computationally complex since it extracts a feature, called the instantaneous dominant meshing multiplying, using STFT, and then resamples the original signal using this feature, decomposes the resampled signal into intrinsic mode functions (IMF) using EMD, and finally carries out the envelope demodulation of the IMF, with the highest kurtosis value, to determine bearing fault.…”

Section: Introductionmentioning

confidence: 99%

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Khan

Kim

2016

Shock and Vibration

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Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A -nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

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Section: Introductionmentioning

confidence: 99%

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Khan

Kim

2016

Shock and Vibration

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“…Through Monte Carlo experiments, literature [19,20] proved that the EMD method has the band pass filter characteristics of the constant quality factor which is similar to the wavelet decomposition; its cut-off frequency and bandwidth are changed with the change of signal. According to the literature [21], the IMF component obtained by EMD decomposition contains different characteristic time scales, and the frequency resolution of the th IMF can be expressed in the following formula:…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

Zhao

Guo

Zhang

2017

Journal of Advanced Transportation

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In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.

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“…However, rotating machinery sometimes works under timevarying speed conditions. In such conditions, the amplitude and fault characteristic frequency (FCF) of the rolling element bearing vibration signal are influenced by the timevarying speed [11]. Hence, envelope analysis and other enhancement techniques based on constant speed conditions cannot be applied directly.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

Hou

Lee

2019

Shock and Vibration

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Fault diagnosis and failure prognostics for rolling element bearing are helpful for preventing equipment failure and predicting the remaining useful life (RUL) to avoid catastrophic failure. Spall size is an important fault feature for RUL prediction, and most research work has focused on estimating the fault size under constant speed conditions. However, estimation of the defect width under time-varying speed conditions is still a challenge. In this paper, a method is proposed to solve this problem. To enhance the entry and exit events, the edited cepstrum is used to remove the determined components. The preprocessed signal is resampled from the time domain to the angular domain to eliminate the effect of speed variation and measure the defect size of a rolling element bearing on outer race. Next, the transient impulse components are extracted by local mean decomposition. The entry and exit points when the roller passes over the defect width on the outer race were identified by further processing the extracted signal with time-frequency analysis based on the continuous wavelet transform. The defect size can be calculated with the angle duration, which is measured from the identified entry and exit points. The proposed method was validated experimentally.

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Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

Cited by 7 publications

References 14 publications

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

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