Real-time and energy-efficient bearing fault diagnosis using discriminative wavelet-based fault features on a multi-core system

This paper presents a comprehensive bearing fault diagnosis methodology to prevent unscheduled interruptions in machinery. This method consists of fault signature extraction, discriminative fault feature selection (not only to improve diagnosis but also to reduce computational overhead), and decision making using a k-nearest neighbor classifier. Although the presented method yields very accurate classification, its computational complexity limits its use in real-time applications. To address that issue, this paper introduces a single instruction, multiple data-based multi-core system including 64 low-cost processing elements to support the high computational fault diagnosis method, where the multi-core system is implemented in a Xilinx Virtex-7 field programmable gate array device, operating at 200 MHz clock frequency. In addition, this study compares the performance of the multi-core system with that of high-performance digital signal processors (DSPs) to demonstrate improvement in execution time and energy efficiency. Experimental results using one-second acoustic emission signals sampled at 1 MHz indicate that the multi-core approach achieves 82.5× and 7.9× speedups over Texas Instruments (TI) TMS320C6748 and TMS320C6671, respectively, when executing the same bearing fault diagnosis algorithm. In addition, the presented multi-core approach reduces 21.5× and 4.2x less energy than TMS320C6748 and TMS320C6671, respectively.