Engineering

Materials science

Acoustic emission

Anomaly detection

Computer science

Acoustics

Anomaly (physics)

Lubrication

Mechanical engineering

Physics

Pattern recognition (psychology)

Abrasion (mechanical)

Condensed matter physics

Artificial intelligence

Composite material

Bearing (navigation)

Convolutional neural network

Tribology International

The present study aims at monitoring and classifying the multi-variant wear behavior of sliding bearings. For this purpose, acoustic emission (AE) technique was applied to a test rig for sliding bearings. AE signals were evaluated with machine learning methods in order to detect anomalies from a hydrodynamic bearing operation. Furthermore, a deep learning approach based on convolutional neural networks was used for multi-class classification into three different wear failure modes, namely running-in, inadequate lubrication and particle-contaminated oil. A high accuracy and high sensitivity have been achieved in the detection and classification of three-body abrasion due to particle contamination. In the cases of running-in and inadequate lubrication, the incubation period during the onset of inadequate lubrication is sometimes mistaken for running-in and vice-versa, which reduces the overall accuracy of the classification.

Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems