Early detection of bearing faults using minimum entropy deconvolution adjusted and zero frequency filter

• Published in: Journal of vibration and control Vol. 28; no. 9-10; pp. 1011 - 1024
• Main Authors: Kumar, Keshav, Shukla, Sumitra, Singh, Sachin K
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-05-2022; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Bearing strength; Bearings; Deconvolution; Entropy; Fault detection; Frequency filters; Impulses; Random noise; Roller bearings; Subtraction; Time domain analysis; Time measurement; Vibration; weak fault detection; distant location of vibration sensor; Bearing fault; zero frequency resonator; convolution adjustment; minimum entropy deconvolution
• ISSN: 1077-5463; 1741-2986
• DOI: 10.1177/1077546320986368

A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero frequency filtering of the bearing vibration signal keeps only the localized disturbances at the impulse locations while attenuating the non-impulsive components of the signal. The effectiveness of zero frequency filtering depends on the strength of impulses present in the measured faulty bearing signal in time domain. In the present work, Minimum entropy deconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. The algorithm is also validated using experimental bearing vibration dataset. Results from the proposed algorithm are compared with the results of the zero frequency filter and local mean subtraction-based technique for rolling element bearings’ fault identification. The proposed algorithm performs better detection in case of a weak fault signal.