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.

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.

scholarly journals Analytical model and spectral characteristics of acoustic emission signal produced by localized fault of rolling element bearing

2021 ◽  
Vol 39 (4) ◽  
pp. 831-838
Author(s):  
Fazhong Li ◽  
Zengshui He ◽  
Lin Zhang ◽  
Anbo Ming ◽  
Yongsheng Yang
Keyword(s):  
Acoustic Emission ◽  
Analytical Model ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Emission Signal ◽  
Rolling Element ◽  
Excitation Mechanisms ◽  
Element Bearing

The accurate description of acoustic emission signals produced by the localized fault of a rolling element bearing plays an important role in its feature extraction and analysis. This paper analyzes the excitation mechanisms and develops the analytical model of acoustic emission signals produced when the rolling element bearing passes across the localized fault on the inner or outer race. Based on the analytical model, the spectral characteristics are discussed substantially. Simulations and experiments are carried out to validate the efficacy of the model developed in the paper. The experimental results show that the response signal thus produced has two parts. The first one is produced by the entry of the rolling element bearing, while the other is produced by the departure of the rolling element bearing. The energy of both parts is concentrated around the resonance frequency of the acoustic emission transducer. Generally, the interval of adjacent acoustic emission events is not equivalent to each other and the corresponding spectrum is continuous in the high frequency band.

A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

2012 ◽  
Vol 227 (6) ◽  
pp. 1362-1370 ◽  
Author(s):  
Changqing Shen ◽  
Qingbo He ◽  
Fanrang Kong ◽  
Peter W Tse
Keyword(s):  
Fault Diagnosis ◽  
Morphological Analysis ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Characteristic

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

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.

Advanced Rolling-Element Bearing Diagnostics Based on Cyclic Spectral Analysis

2007 ◽  
Vol 347 ◽  
pp. 265-270
Author(s):  
Jerome Antoni ◽  
Roger Boustany
Keyword(s):  
Background Noise ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Spectral Signatures ◽  
Bearing Diagnostics ◽  
Rolling Element ◽  
Cyclical Behaviour ◽  
Element Bearing ◽  
Incipient Fault ◽  
Inner Race

Rolling-element bearing vibrations are random cyclostationary, that is they exhibit a cyclical behaviour of their statistical properties while the machine is operating. This property is so symptomatic when an incipient fault develops that it can be efficiently exploited for diagnostics. This paper gives a synthetic but comprehensive discussion about this issue. First, the cyclostationarity of bearing signals is proved from a simple phenomenological model. Once this property is established, the question is then addressed of which spectral quantity can adequately characterise such vibration signals. In this respect, the cyclic coherence - and its multi-dimensional extension in the case of multi-sensors measurements -- is shown to be twice optimal: first to evidence the presence of a fault in high levels of background noise, and second to return a relative measure of its severity. These advantages make it an appealing candidate to be used in adverse industrial environments. The use and interpretation of the proposed tool are then illustrated on actual industrial measurements, and a special attention is paid to describe the typical "cyclic spectral signatures" of inner race, outer race, and rolling-element faults.

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%.

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