Demodulation of Vibration Signal Based on Envelope-Kurtogram for Ball Bearing Fault Detection

Berli Paripurna Kamiel

Abstract


Rolling element bearings often suffer damage due to harsh operating and environmental conditions. The method commonly used in detecting faults in a bearing is envelope analysis. However, this method requires setting the central frequency and the correct bandwidth - which corresponds to the resonance frequency of the bearing - for signal demodulation to be effective. This study proposes a kurtogram to determine the correct central frequency and bandwidth to obtain the frequency band with the highest impulse content or the highest kurtosis value. Analysis envelope is applied to the filtered vibration signal using the central frequency and bandwidth parameters obtained from the kurtogram. The results showed that the envelope-kurtogram method is effective for faulty bearing detection as shown in the envelope spectrum where the peaks coincide with the bearing defect characteristic frequency (BPFO) with high accuracy. Likewise, it can be observed several BPFO harmonics which provide information on the level of bearing fault.

Keywords


Kurtogram; envelope analysis; demodulation; central frequency; bandwidth

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References


Du W, Wang Z, Gong X, Wang L, Luo G. Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump. Shock and Vibration. 2016;2016:1248626.

Qiu M, Li W, Zhu Z, Jiang F, Zhou G. Fault Diagnosis of Bearings with Adjusted Vibration Spectrum Images. Shock and Vibration. 2018;2018:6981760.

Wen-Chang T, Yi-Fan L, Min-Chun P, Resonant-frequency band choice for bearing fault diagnosis based on EMD and envelope analysis. 2010 8th World Congress on Intelligent Control and Automation; 2010 7-9 July 2010.

Zhang X, Kang J, Zhao J, Zhao J, Teng H. Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram. Journal of Vibroengineering. 2015;17(6):3023-34.

Kim S, An D, Choi J-H. Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB. Applied Sciences. 2020;10(20):7302.

Duan J, Shi T, Zhou H, Xuan J, Zhang Y. Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings. Sensors (Basel, Switzerland). 2018;18(5):1466.

Senanayaka JSL, Khang HV, Robbersmyr KG, Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm. 2017 20th International Conference on Electrical Machines and Systems (ICEMS); 2017 11-14 Aug. 2017.

Xu L, Chatterton S, Pennacchi P. A Novel Method of Frequency Band Selection for Squared Envelope Analysis for Fault Diagnosing of Rolling Element Bearings in a Locomotive Powertrain. Sensors (Basel, Switzerland). 2018;18(12):4344.

Antoni J, Randall RB. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing. 2006;20(2):308-31.

Sui W, Zhang D, Research on envelope analysis for bearings fault detection. 2010 5th International Conference on Computer Science & Education; 2010 24-27 Aug. 2010.

Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing. 2007;21(1):108-24.




DOI: https://doi.org/10.18196/jmpm.v4i2.11271

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