Pattern Recognition of Bearing Faults using Smoother Statistical Features
This work addresses bearing fault diagnosis for industrial maintenance, but it is incremental as it builds on existing pattern recognition methods with a smoothing technique.
The authors tackled the problem of identifying bearing faults by developing a pattern recognition diagnostic scheme that uses smoothed statistical features from vibration data, which enhanced diagnostic accuracy by reducing the impact of vibration randomness.
A pattern recognition (PR) based diagnostic scheme is presented to identify bearing faults, using time domain features. Vibration data is acquired from faulty bearings using a test rig. The features are extracted from the data, and processed prior to utilize in the PR process. The processing involves smoothing of feature distributions. This reduces the undesired impact of vibration randomness on the PR process, and thus enhances the diagnostic accuracy of the model.