Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning

The detection of contaminants in the lubricant of rolling element bearings using acoustic emission signals is a challenging problem, in particular at high rotational speeds. This problem calls for new analysis methods beyond the conventional amplitude- and frequency-based methods. Feature learning is successfully used in the machine learning field to characterize complex signals. Here we use an unsupervised feature learning approach to distinguish acoustic emission signals. We investigate the repetition rates of features identified with shift-invariant dictionary learning and find that the signature of contaminated lubricant is significantly stronger than the effect on conventional condition indicators like the RMS and the enveloped RMS at rotational speeds above 300 rpm and up to 3000 rpm.