A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions

Borghesani, Pietro; Ricci, R.; Chatterton, Steven; Pennacchi, Paolo

doi:10.1016/j.ymssp.2012.09.014

Cited by 173 publications

(96 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This can be evaluated with the following steps. 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).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This method can also be used to achieve a stationary signal in the angular domain rather than a nonstationary signal in the time domain. Borghesani et al [19] applied envelope analysis, based on order tracking, to a nonstationary signal and introduced prewhitening to improve the signal-to-noise ratio. Consequently, the benefit of applying this method to fault feature extraction for rolling bearings has been well proven.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The test dataset whose alarm state is unknown is taken as the input of -means algorithm. Cluster center = ( 1 , 2 , ) can be obtained according to (12). This cluster center can denote the operating state of the equipment steadily and reduce false alarms caused by few outliers as much as possible.…”

Section: (4) Determination Of Cluster Center Of the Test Data Whosementioning

confidence: 99%

“…To solve issues caused by variable operating conditions for dual-rotor equipment, a large number of researches focus on how to extract fault features from nonstationary signals in time domain [11][12][13]. So far, those researches have brought about great benefits.…”

Section: Introductionmentioning

confidence: 99%

A SVDD and K‐Means Based Early Warning Method for Dual‐Rotor Equipment under Time‐Varying Operating Conditions

Jiang

Feng

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

Under frequently time-varying operating conditions, equipment with dual rotors like gas turbines is influenced by two rotors with different rotating speeds. Alarm methods of fixed threshold are unable to consider the influences of time-varying operating conditions. Hence, those methods are not suitable for monitoring dual-rotor equipment. An early warning method for dual-rotor equipment under time-varying operating conditions is proposed in this paper. The influences of time-varying rotating speeds of dual rotors on alarm thresholds have been considered. Firstly, the operating conditions are divided into several limited intervals according to rotating speeds of dual rotors. Secondly, the train data within each interval is processed by SVDD and the allowable ranges (i.e., the alarm threshold) of the vibration are determined. The alarm threshold of each interval of operating conditions is obtained. The alarm threshold can be expressed as a sphere, whose controlling parameters are the coordinate of the center and the radius. Then, the cluster center of the test data, whose alarm state is to be judged, can be extracted through -means. Finally, the alarm state can be obtained by comparing the cluster center with the corresponding sphere. Experiments are conducted to validate the proposed method.

show abstract

“…Therefore, the envelope analysis (or resonant demodulation technique) has been early developed to demodulate the signal to obtain a representation of the impulse train. Considering its effectiveness, simplicity in application, and the low computation, envelope analysis has gained a leading role among the techniques applied for rolling element bearing diagnostics in the last years [11]. However, the feature of rolling bearing early fault is very weak and is often buried in the strong background noises.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Lin

Shangfei

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

show abstract

A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions

Cited by 173 publications

References 11 publications

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

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

A SVDD and K‐Means Based Early Warning Method for Dual‐Rotor Equipment under Time‐Varying Operating Conditions

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Contact Info

Product

Resources

About