The use of spectral kurtosis as a trend parameter for bearing faults diagnosis

Author(s):  
Lotfi Saidi ◽  
Jaouher Ben Ali ◽  
Farhat Fnaiech
2018 ◽  
Vol 27 (4) ◽  
pp. 1166-1173 ◽  
Author(s):  
I. Andrijauskas ◽  
M. Vaitkunas ◽  
R. Adaskevicius

2018 ◽  
Vol 17 (5) ◽  
pp. 1192-1212 ◽  
Author(s):  
Faris Elasha ◽  
Matthew Greaves ◽  
David Mba

Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviours that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal-to-noise ratio in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging while operating within a helicopter gearbox. In addition, this article investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and acoustic emissions. It compares their effectiveness for various operating conditions. Three signal processing techniques, including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using acoustic emission for helicopter gearbox monitoring.


2009 ◽  
Vol 40 (3-4) ◽  
pp. 393-402 ◽  
Author(s):  
Khalid F. Al-Raheem ◽  
Asok Roy ◽  
K. P. Ramachandran ◽  
D. K. Harrison ◽  
Steven Grainger

2015 ◽  
Vol 62 (3) ◽  
pp. 1855-1865 ◽  
Author(s):  
Valeria C. M. N. Leite ◽  
Jonas Guedes Borges da Silva ◽  
Giscard Francimeire Cintra Veloso ◽  
Luiz Eduardo Borges da Silva ◽  
Germano Lambert-Torres ◽  
...  

Author(s):  
Xiaohui Chen ◽  
Lei Xiao ◽  
Xinghui Zhang ◽  
Zhenxiang Liu

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.


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