Abstract
This paper proposes the development of a unique method for monitoring procedures for rolling element bearing. Mechanical signals can be classified as Gaussian and non-Gaussian noise. Both of these noises obstruct the detection of rolling bearing defects using customary techniques. Analysis of bispectrum helps to filter out the Gaussian noise. The main focus of the paper is the removal of the non-Gaussian noise signal and thereafter due to noise in the signal methods like empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is carried out. In this paper, it is observed that EMD undergoes the problem of mode mixing and is incapacitated of separating mode frequencies present in octave. So, we look for another method, i.e. ensemble empirical mode decomposition method (EEMD). This reduces the non-Gaussian noise effectively with the addition of white noise to the EMD several times. Also, it is noticed from simulation results that masking of EMD helps in separating modes of the signal. So, EEMD and masking EMD have been suggested in this work to determine bearing defect.
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Singh, A., Satyajit, K.R., Ray, P. (2022). A Computational Intelligence-Based Novel Bearing Defect Detection Method. In: Kumar, S., Singh, B., Singh, A.K. (eds) Recent Advances in Power Electronics and Drives. Lecture Notes in Electrical Engineering, vol 852. Springer, Singapore. https://doi.org/10.1007/978-981-16-9239-0_49
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DOI: https://doi.org/10.1007/978-981-16-9239-0_49
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