Notice of RetractionMulti-fault diagnosis of ball bearing using intrinsic mode functions, Hilbert marginal spectrum and multi-class support vector machine

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

A number of techniques for detection of faults in ball bearing using frequency domain approach exist today. For analyzing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier Transform (DFT) has been known to be less efficient. One of the most suited time-frequency approach; wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert-Huang Transform (HHT), technique provides multi-resolution in various frequency scales and takes the signal´s frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). HHT is effective in many different fields but lacks proper theoretical support. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In this paper Firstly, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary. Secondly, we choose some special IMFs to obtain Hilbert transform and then Hilbert marginal spectrum and the last local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Finally, the characteristic amplitude ratios serve as the fault characteristic vectors to- be input to the multi-class support vector machine (MSVM) classifiers and the work condition and fault patterns of the roller bearings and then faults are diagnosis real time based on Voting.