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This paper proposes the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears. The AR filter technique has been proven superior for detecting localised gear tooth faults than the traditionally used residual analysis technique. The AR filter technique is based on subtracting a regular gearmesh signal, as represented by the toothmesh harmonics and immediately adjacent sidebands, from the spectrum of a signal from one gear obtained by the synchronous signal averaging technique (SSAT). The existing AR filter technique performs well but is based on autocorrelation measurements and is thus insensitive to phase relationships which can be used to differentiate noise from impulses. The MED technique can make a use of the phase information by means of the higher-order statistical (HOS …