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

摘要

Purpose 一 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. Design/methodology/approach - 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. Findings - 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. Research limitations/implications - 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. Practical implications ― 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. Originality/value - The application of the feature reduction technique will provide manufacturers and researchers with a new means of improving fault detection in machinery components.