Cyclic Statistics Based Neural Network for Early Fault Diagnosis of Rolling Element Bearings

摘要: This paper proposes a novel cyclic statistics based artificial neural network for early fault diagnosis of rolling element bearing, via which the real time domain signals obtained from test rig are preprocessed by to perform monitoring diagnosis. Three kinds familiar faults intentionally introduced in order investigate typical bearing faults. The testing results presented and discussed with examples data collected rig.