Vibration‐ and acoustic‐emissions based novelty detection of fretted bearings

The purpose of this paper is to examine the use of a new feature reduction technique with novelty detection on vibration and acoustic‐emission sensors monitoring bearings mounted in the test benches of automotive manufacturers.

Signals from standard accelerometers and acoustic‐emission sensors were gathered from bearings operating under steady conditions on an accessory‐drive test bench. The bearings under test were subject to a variety of faults including fretting. These signals were processed and reduced to standard feature vectors, the dimensionality of which was reduced using a new principal‐component‐like technique optimized for novelty detection. The reduced data were analyzed with a novelty detection technique called the Support Vector Data Descriptor.

The classification results from these sensors, after being reduced with the proposed feature reduction technique, are substantially improved over those achievable with only standard novelty detection; nearly zero‐percent classification error was achieved.

The feature reduction technique depends, in part, on the availability of the fault type in question – potentially violating the normal novelty detection assumption of limited abnormal data. This may require the manufacturer to gather real or simulated fault data prior to running tests.

Incipient faults may be detectable at a much earlier stage in a manufacturer's component failure analysis. Test engineers may use this technique to reliably automate the fault detection process and enable improved root‐cause analysis through the earlier identification of faults.

The application of the feature reduction technique will provide manufacturers and researchers with a new means of improving fault detection in machinery components.