Many-core accelerated local outlier factor based classifier in bearing fault diagnosis

This paper proposes a feature extraction, selection, and classification based bearing fault diagnosis methodologies using acoustic emission (AE) signal. First, a set of statistical, time-domain, and frequency domain features are extraction from AE signal. Of these features, some features having more informative data to distinguish faults are selected. Finally, a classifier based on local outlier factor (LOF) is used to detect faults of bearing. LOF consists of calculating distances of each input will all the training features, thus having a lot of computations. To reduce execution time, this paper implemented the LOF on a data parallel many-core architecture. Experimental results showed that, many-core implementation of LOF algorithm is more than 923× faster than sequential implementation.