Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement

2016 ◽  
Vol 60 ◽  
pp. 274-284 ◽  
Author(s):  
Chuan Li ◽  
Vinicio Sanchez ◽  
Grover Zurita ◽  
Mariela Cerrada Lozada ◽  
Diego Cabrera
Keyword(s):  
Defect Detection ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Bearing Defect ◽  
Synchrosqueezing Transform ◽  
Rolling Element ◽  
Element Bearing

Rolling Element Bearing Defect Detection and Diagnostics Using Displacement Transducers

2001 ◽  
Author(s):  
John J. Yu ◽  
Donald E. Bently ◽  
Paul Goldman ◽  
Kenwood P. Dayton ◽  
Brandon G. Van Slyke
Keyword(s):  
Defect Detection ◽  
Amplitude Ratio ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Time Base ◽  
Outer Race ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Displacement Transducers

This paper introduces the methodology of rolling element bearing defect detection using high-gain displacement transducers. The nature of defect influence on the outer race deflection in the vicinity of the transducer tip in time base has been established. Inner race, outer race, and rolling element (ball/roller) defects, which often occur sequentially, can be clearly identified according to spike signals on the time-varying outer race deflection curve along with known bearing frequencies. The developed techniques are fully corroborated by experimental data. Spike-to-deflection amplitude ratio, which is almost independent of changes in speed and load for a given defect, is used to judge the defect severity. Spectral characteristics due to these defects have also been found. It is shown that this direct measurement by using displacement transducers without casing influence, which would be inevitable by using accelerometers mounted on the casing, is a reliable approach to detect bearing defects as well as their severity and locations.

Rolling Element Bearing Defect Detection and Diagnostics Using Displacement Transducers

2002 ◽  
Vol 124 (3) ◽  
pp. 517-527 ◽  
Author(s):  
J. J. Yu ◽  
D. E. Bently ◽  
P. Goldman ◽  
K. P. Dayton ◽  
B. G. Van Slyke
Keyword(s):  
Defect Detection ◽  
Amplitude Ratio ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Time Base ◽  
Outer Race ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Displacement Transducers

This paper introduces the methodology of rolling element bearing defect detection using high-gain displacement transducers. The nature of defect influence on the outer race deflection in the vicinity of the transducer tip in time base has been established. Inner race, outer race, and rolling element (ball/roller) defects, which often occur sequentially, can be clearly identified according to spike signals on the time-varying outer race deflection curve along with known bearing frequencies. The developed techniques are fully corroborated by experimental data. Spike-to-deflection amplitude ratio, which is almost independent of changes in speed and load for a given defect, is used to judge the defect severity. Spectral characteristics due to these defects have also been found. It is shown that this direct measurement by using displacement transducers without casing influence, which would be inevitable by using accelerometers mounted on the casing, is a reliable approach to detect bearing defects as well as their severity and locations.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Guang-Quan Hou ◽  
Chang-Myung Lee
Keyword(s):  
Research Work ◽  
Defect Size ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Time Varying ◽  
Entry And Exit ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

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.

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor

2013 ◽  
Vol 588 ◽  
pp. 333-342 ◽  
Author(s):  
Leon Swędrowski ◽  
Kazimierz Duzinkiewicz ◽  
Michał Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Neural Networks ◽  
Induction Motor ◽  
Measurement Data ◽  
Failure Detection ◽  
Rolling Element Bearing ◽  
Bearing Defect ◽  
Instantaneous Current ◽  
Rolling Element ◽  
Current Change ◽  
Element Bearing

Bearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect transformation, namely Clark transformation. It determines the vector of the spatial stator current based on instantaneous current measurements of the induction motor supply phases current. The analysis of the processed measurement data used multilayered, one-directional neural networks, which are particularly attractive due to their nonlinear structure and ability to learn. During the research 40 bearings: undamaged, with damages of three types and various degrees of fault extent, were used. The conducted research proves the efficiency of neural networks for detection and recognition of faults in induction motor bearings. In case of tests of the unknown state bearings, an efficiency approach to failure detection equaled 77%.

scholarly journals Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission

2018 ◽  
Vol 8 (5) ◽  
pp. 3560
Author(s):  
Sudarsan Sahoo ◽  
J. K. Das ◽  
Bapi Debnath
Keyword(s):  
Condition Monitoring ◽  
Acoustic Signal ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Second Stage ◽  
Acoustic Signature ◽  
Bearing Condition Monitoring ◽  
Rolling Element ◽  
Element Bearing ◽  
Two Stages

The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.

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