Envelope order tracking for fault detection in rolling element bearings

Guo, Yu; Liu, Ting‐Wei; Na, Jing; Fung, Rong‐Fong

doi:10.1016/j.jsv.2012.07.026

Cited by 79 publications

(44 citation statements)

References 16 publications

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“…Based on the kurtogram and the parameter of the non-Gaussian alpha-stable model, a method for bearing fault detection was given in [71]. An envelope order tracking (EOT) analysis scheme was proposed for the fault detection of rolling element bearing under varying-speed running condition in [72], where the kurtogram algorithm was utilized to obtain both optimal center frequency and bandwidth of the band-pass filter. By means of the determination of the center frequency and bandwidths, a system defined by the Duffing equation in the presence of defective bearing signal was developed in [73], where the state changes of the rolling element bearing can be identified using the phase plane trajectories and Lyapunov exponents of Duffing equation.…”

Section: Combinations With Other Methods For Bearing Fault Detectionmentioning

confidence: 99%

“…SK for frequency band selection in bearing diagnostics was compared with SVM in [88], in which these two totally different approaches came to the similar conclusions. CMWT-based SK [70] SK for the selection of a frequency band Alpha-stable model Kurtogram [71] Kurtogram was generated using α parameter EOT Kurtogram [72] Kurtogram was used as preprocessing to determine signatures Duffing equation…”

Section: Comparisons With Other Techniques For Bearing Fault Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Wang

Xiang

Markert

et al. 2016

Mechanical Systems and Signal Processing

394

142

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Section: Combinations With Other Methods For Bearing Fault Detectionmentioning

confidence: 99%

Section: Comparisons With Other Techniques For Bearing Fault Detectionmentioning

confidence: 99%

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Wang

Xiang

Markert

et al. 2016

Mechanical Systems and Signal Processing

394

142

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“…First, angle θ i can be obtained using Equations (18) and (19) based on the kth sampling point i, as well as the shaft rotating angle θ t i at time t i Equations. Next, the even angle series θ n can be evaluated from Equations (20) and (22). Then, the sampling time of the even angle can be determined through piecewise linear interpolation, which can be evaluated using the following equation:…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, this method mainly dealt with traditional REBs at slightly fluctuant operating speed, without considering two different rotating speeds and sharp changes in speeds. Liu et al [22] combined SK and order tracking with envelope demodulation to effectively diagnose the faults in rolling bearings of rotating machinery working at variable speeds. However, this method is mainly suitable for equipment with a single rotor and can be utilized when the vibration signal is directly measured at the housing of the faulty bearing.…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Jiang¹,

HU²,

Feng³

et al. 2017

Applied Sciences

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Abstract:Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by the elastic supports and complex structure of the thin-walled strut. The vibration signal is severely weakened during transmission under the influences of the transfer path. In the meantime, in the vibration of other components, a huge amount of noise is produced by the air flow, and the variable speeds of the inner and outer rings of the intershaft bearings make it harder to analyze the signal. Hence, it is very difficult to extract the vibration fault features of intershaft bearings. To deal with the variable speed of dual rotors, as well as the weak signal, a fault feature extraction scheme for the weak fault signals of intershaft bearings is proposed in this paper. This scheme is based on linear prediction, spectral kurtosis, and order tracking in the rotation speed difference domain. First, a prewhitening process, based on linear prediction, is applied to the fault signal of the intershaft bearings to eliminate the stationary component. Thus, the remaining components, including the impulse signal of faulty bearings and nonstationary noise, can retain the features of the vibrational bearings, in addition to reducing the noise. Second, the optimal center frequency and bandwidth of the band-pass filter, applied to resonant demodulation, are selected by spectral kurtosis. Subsequently, the enveloped signal containing the features of the faults found in the intershaft bearings is obtained by resonance demodulation. The quasi-stationary signal in the angle domain is acquired by the even angle resampling of the nonstationary envelope signal, as a result of the variable speed. The final order spectrum is obtained through a Fourier transform. Fault diagnosis can be conducted for the intershaft bearings by comparing this spectrum with the feature order of the bearing fault. Experiments were conducted to verify the validity of the proposed scheme.

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“…However, these methods cannot be directly applied to bearing fault diagnosis under variable rotational speed conditions. Time-varying speed running conditions of rolling element bearing are very common in industry [5,6]. Time-varying speed running conditions refer to these kinds of conditions that a wide range of speed changing process such as parking and staring process of rotating machines, and speed fluctuation influenced by load and working condition.…”

Section: Introductionmentioning

confidence: 99%

Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

Zhao

Chen

et al. 2016

Journal of Sound and Vibration

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Envelope order tracking for fault detection in rolling element bearings

Cited by 79 publications

References 16 publications

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

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