Two level de-noising algorithm for early detection of bearing fault using wavelet transform and zero frequency filter

Abstract

Abstract Rolling element bearings are used widely in rotating machinery such as, generators, motors, pumps, and turbines. Early detection of bearing fault is necessary for preventing machinery breakdown. Localised fault in a rolling element bearing gives rise to periodic impulses in vibration signal. At early stage vibration signal is weak. A zero frequency filter (ZFF) and wavelet transform based two level de-noising algorithm is proposed for the identification of these periodic impulses. First level de-noising is performed by ZFF. Envelope signal is passed through the zero frequency filter to enhance the impulse information and attenuate the noise and sinusoids. Second level de-noising is performed by wavelet transform. Wavelet transform helps in extracting the impulse information which is hidden in large amplitude of ZFF output. An expression is developed for optimum level of wavelet decomposition. Working of the algorithm is explained through simulated signal with periodic impulses. The algorithm is verified with experimental datasets of both seeded and naturally grown bearing faults.