identification of bearing faults using time domain zero-crossings

in this paper, zero-crossing characteristic features are employed for early detection and identification of single point bearing defects in rotating machinery. as a result of bearing defects, characteristic defect frequencies appear in the machine vibration signal, normally requiring spectral analysis or envelope analysis to identify the defect type. zero-crossing features are extracted directly from the time domain vibration signal using only the duration between successive zero-crossing intervals and do not require estimation of the rotational frequency. the features are a time domain representation of the composite vibration signature in the spectral domain. features are normalized by the length of the observation window and classification is performed using a multilayer feedforward neural network. the model was evaluated on vibration data recorded using an accelerometer mounted on an induction motor housing subjected to a number of single point defects with different severity levels.