Cyclo-non-stationary Based Bearing Diagnostics of Planetary Gearboxes

2019 ◽  
pp. 343-352
Author(s):  
Alexadre Mauricio ◽  
Wade Smith ◽  
Junyu Qi ◽  
Robert Randall ◽  
Konstantinos Gryllias
Keyword(s):  
Bearing Diagnostics

Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farms ◽  
Operating Conditions ◽  
Vibration Data ◽  
Bearing Diagnostics ◽  
Scattering Transform ◽  
The One ◽  
Envelope Spectrum ◽  
High Effort

Abstract As a renewable, unlimited and free resource, wind energy has been intensively deployed in the past to generate electricity. However, the maintenance of Wind Turbines (WTs) can be challengeable. On the one hand, most wind farms operate in remote areas and on the other hand, the dimension of WTs’ tip/hub/rotor are usually enormous. In order to prevent abrupt breakdowns of WTs, a number of Condition Monitoring (CM) methods have been proposed. Focusing on bearing diagnostics, Squared Envelope Spectrum is one of the most common techniques. Moreover in order to identify the optimum demodulation frequency band, fast Kurtogram, Infogram and Sparsogram are nowadays popular tools evaluating respectively the Kurtosis, the Negentropy and the Sparsity. The analysis of WTs usually requires high effort due to the complexity of the drivetrain and the varying operating conditions and therefore there is still need for research on effective and reliable CM techniques for WT monitoring. Thus the purpose of this paper is to investigate a blind and effective CM approach based on the Scattering Transform. Through the comparison with state of the art techniques, the proposed methodology is found more powerful to detect a fault on six validated WT datasets.

Comparison Between Time-Frequency Techniques to Predict Ball Bearing Faults in Drives Executing Arbitrary Motion Profiles

2008 ◽  
Author(s):  
Marco Cocconcelli ◽  
Cristian Secchi ◽  
Riccardo Rubini ◽  
Cesare Fantuzzi ◽  
Luca Bassi
Keyword(s):  
Velocity Profile ◽  
Cross Correlation ◽  
Fault Prediction ◽  
Variable Velocity ◽  
Outer Race ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Bearing Diagnostics ◽  
Packaging Machine ◽  
Expected Signal

In this paper Wavelet Transform (WT) and Hilbert-Huang Transform (HHT) are used as bearing diagnostics tools in drives executing arbitrary motion profiles. This field is increasingly drawing the attention of the industries because the modern electric motors work as electric cams inducing the shaft to move with a cyclic variable-velocity profile. The literature papers take into account only a constant velocity profile and they are not suitable for such applications. In fact literature methods analyse the signal only in the frequency domain, while in variable-velocity condition the bearing diagnostics should be performed in time domain. Both WT and HHT are time-frequency techniques which describe an input signal as a sum of specific functions. These functions are compared with a signal which simulates the expected vibrations of a bearing with a given fault, e.g. on the outer race. The comparison is done through a cross-correlation between the expected signal and the time-frequency techniques output. WT and HHT are used separately in an industrial case, which consists in bearing fault prediction in an automated packaging machine. In the end of the paper the WT and HHT results are discussed to analyse the different responses.

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.

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