The main hindrance in easy detection of bearing faults from vibration data is that the signal is noise ridden, and only an efficient method for noise reduction will effectively bring out the fault characteristics. This paper proposes a novel method for such noise reduction using Lucy–Richardson deconvolution, which is an iterative technique for deblurring images. Its application in signal processing more specially in bearing fault diagnosis is being studied in this paper. The characteristics of this deconvolution with different shapes of point spread function and their effectiveness are also shown.
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