Differential Diagnosis of Gear and Bearing Faults

2002 ◽  
Vol 124 (2) ◽  
pp. 165-171 ◽  
Author(s):  
J. Antoni ◽  
R. B. Randall
Keyword(s):  
Differential Diagnosis ◽  
Autocorrelation Function ◽  
Diagnostic Procedure ◽  
Rolling Element Bearings ◽  
Comprehensive Model ◽  
Bearing Faults ◽  
Rolling Element ◽  
Distributed Faults

This paper deals with the vibration-based diagnosis of rolling element bearings in the presence of strong interfering gear signals, such as is typical of helicopter gearboxes. The key idea consists in recognizing gear signals as purely periodic, whereas bearing signals experience some randomness and are close to cyclostationary, i.e. with a periodic bivariate autocorrelation function. This assertion is demonstrated by introducing a comprehensive model for the vibration generating process of bearing faults: distinctions are made between localized and distributed faults, between cyclostationary and pseudo-cyclostationary processes, and between additive and multiplicative interactions with gear signals. Finally, an original diagnostic procedure is proposed and its performance illustrated using simulated, experimental and actual cases.

Study on a Novel Bearing Fault Diagnosis Method from Frequency and Energy Perspective

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huiming Zhao ◽  
Wu Deng
Keyword(s):  
Wavelet Packet ◽  
Bottom Layer ◽  
Wavelet Packet Decomposition ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Organic Combination ◽  
Component Faults

AbstractEarly identification of faults in rolling element bearings is a challenging task; especially extracting transient characteristics from a noisy signal and identifying bearings fault become critical steps. In this paper, a novel method for real time fault detection in rolling element bearings is proposed to deal with non-stationary fault signals from frequency and energy perspective. Second-order blind identification (SOBI) and wavelet packet decomposition are organically integrated to diagnose the early bearing faults, the fault vibration signals are processed by SOBI algorithm, and feature information is extracted; meanwhile, fault vibration signals are decomposed by the wavelet packet, the energy of terminal nodes(at the bottom layer of wavelet packet decomposition) are analyzed because the energy of terminal nodes has different sensitive to different component faults. Therefore, the bearing faults can be diagnosed by organic combination of fault characteristic frequency analysis and energy of the terminal nodes, and the effectiveness, feasibility and robustness of the proposed method have been verified by experimental data.

A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy

2017 ◽  
Vol 18 (5) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huimin Zhao ◽  
Wu Deng ◽  
Yannan Sun
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Peak Frequency ◽  
Band Pass Filter ◽  
Rolling Element Bearings ◽  
Pass Filter ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Rolling Element ◽  
Diagnosis Method

AbstractRolling element bearings faults may lead to fatal breakdown of machines. Therefore, it is significant to be study bearings diagnosis, and the vibration-based methods have received intensive study because vibration signals collected from bearings carry rich information on machine health conditions, and it is possible to obtain vitalcharacteristic information from the vibration signals through using signal processing techniques. This paper proposes a novel vibration-based diagnosis method about bearing faults, first, a new pattern recognition method is proposed to diagnose bearing faults through using the interval value of the spectral peak frequency in the frequency domain; second, vibration signals of different parts faults of the bearings will be processed by different algorithm for precisely extracting the fault characteristics; and third, in order to extract transient characteristics from a noisy signal, the filter need to be developed and to further improve the signal-to-noise ratio (SNR), band pass filter is designed based on the PSD of vibration signals in this paper. The vibration signals collected from rolling element bearings are used to demonstrate the performance of the proposed method, andthe results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

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