Detection of Generalized-Roughness and Single-Point Bearing Faults Using Linear Prediction-Based Current Noise Cancellation

The dominant components in the stator current of a typical induction motor contain a substantial amount of information that is not related to bearing faults and can be considered as "noise" to bearing fault detection. The presence of the noise in the current signal creates the possibility of missed and false alarms. This paper develops a current noise cancellation method. When noise cancellation is used for recovering a desired signal, the distinguishing characteristics of both the desired signal and the noise need to be considered. Contrary to conventional current noise cancellation methods, "predictability" is considered as the distinguishing characteristic. Linear prediction theory with an optimum prediction order is applied to efficiently model the predictable components as noise. The developed method is able to efficiently cancel out the noise from the current signal. The information about the mechanical load level, the faulty part of the bearing, machine parameters, nameplate values, the current spectrum distribution, and the current data associated with the healthy bearing is not required in this method. Simulation and experimental results confirm the merits and effectiveness of the developed method to detect the bearing fault types through kurtosis and spectral analysis.