Low speed bearing fault diagnosis using acoustic emission sensors

In this paper, a new methodology for low speed bearing fault diagnosis is presented. This acoustic emission (AE) based technique starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies. Then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage. The resampling approach is able to segment the AE signal according to shaft crossing times such that an even number of data points are available to compute a single spectral average which is used to extract features and evaluate numerous condition indicators (CIs) for bearing fault diagnosis. Unlike existing averaging based noise reduction approaches that require the computation of multiple averages for each bearing fault type, the presented approach computes only one average for all bearing fault types. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The results in this paper have shown that the low sampled AE signals in combination with the presented approach can be utilized to effectively extract condition indicators to diagnose all four bearing fault types at multiple low shaft speeds below 10 Hz.