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. ► Signal processing using time, frequency, and sparse/compressive analysis. ► Low power feature extraction algorithms, estimation and detection. ► Multi-modal signal acquisition, spatio-temporal decision fusion. ► Unattended ground sensors, monitoring techniques, fault detection, and diagnosis.