An approach to investigating rolling element bearing damage under impact loading

2021 ◽  
pp. 135065012110594
Author(s):  
Matti Savolainen ◽  
Arto Lehtovaara
Keyword(s):  
Impact Loading ◽  
Rolling Element Bearing ◽  
Acceleration Response ◽  
Test Device ◽  
Outer Race ◽  
Response Data ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Damage ◽  
The Impact

This paper presents an approach to studying rolling element bearing damage under the interference of impact loading. In the experimental part, a series of bearing tests was performed by using the twin-disc test device with artificially damaged bearings. This was followed by analysis of the measured acceleration response data in impact-free condition as well as under the influence of the impact loading. The results showed successful detection of the bearing outer race damage by using typical bearing damage detection approaches regardless whether the impact loading was applied to the system or not. In turn, recognition of the bearing rolling element damage required specific signal processing.

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.

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.

scholarly journals Development of damage detection parameters over the lifetime of a rolling element bearing

2021 ◽  
Vol 38 (3−4) ◽  
Author(s):  
Matti Savolainen ◽  
Arto Lehtovaara
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
Impact Load ◽  
Load Condition ◽  
Constant Load ◽  
Rolling Element Bearing ◽  
Emission Data ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Damage

This paper presents the trends of damage detection parameters over the lifetime of a rolling element bearing. In the experimental part, a series of bearing tests was performed using the twin-disc test device, until the monitored bearing was severely worn. This was followed by the analysis of measured acceleration and acoustic emission data in a constant-load condition, but also as loaded with impact-type loading. The results showed that traditionally used parameters, such as kurtosis and RMS, can indicate whether the bearing is damaged or not in a non-impact load condition. However, especially under impact-loading, the parameters based on acoustic emission data showed good performance and enabled monitoring of progress of the bearing damage.

Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

2021 ◽  
pp. 107754632110507
Author(s):  
HongChao Wang ◽  
WenLiao Du ◽  
Haiyi Li ◽  
Zhiwei Li ◽  
Jiale Hu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Enhancement Effect ◽  
Minimum Entropy ◽  
Rolling Element ◽  
Element Bearing ◽  
Repetitive Impact ◽  
The Impact ◽  
Fault Feature Extraction

As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hu Aijun ◽  
Lin Jianfeng ◽  
Sun Shangfei ◽  
Xiang Ling
Keyword(s):  
Fault Detection ◽  
Finite Impulse Response ◽  
Rolling Element Bearing ◽  
Signal Extraction ◽  
Outer Race ◽  
Bearing Fault ◽  
Novel Approach ◽  
Rolling Element ◽  
Element Bearing ◽  
Early Fault Detection

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.

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