No abstract

No abstract

No abstract

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 82.5x and 7.9x 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 21.5x and 4.2x less energy than TMS320C6748 and TMS320C6671, respectively. Keywords-Acoustic emission; discrete wavelet packet transform; discriminative fault feature selection; energy efficiency; multi-core system; online bearing fault diagnosis I. * Corresponding author 978-1-4799-1894-2/15/$31.00 ©2015 IEEE

Spectral modeling synthesis (SMS) has been used as a powerful tool for musical sound modeling. This technique considers a sound as a combination of a deterministic plus a stochastic component. The deterministic component is represented by the series of sinusoids that are described by amplitude, frequency, and phase functions and the stochastic component is represented by a series of magnitude spectrum envelopes that functions as a time varying filter excited by white noise. These representations make it possible for a synthesized sound to attain all the perceptual characteristics of the original sound. However, sometimes considerable phase variations occur in the deterministic component by using the conventional SMS for the complex sound such as whale sounds when the partial frequencies in successive frames differ. This is because it utilizes the calculated phase to synthesize deterministic component of the sound. As a result, it does not provide a good spectrum matching between original and synthesized spectrum in higher frequency region. To overcome this problem, we propose a modified SMS that provides good spectrum matching of original and synthesized sound by calculating complex residual spectrum in frequency domain and utilizing original phase information to synthesize the deterministic component of the sound. Analysis and simulation results for synthesizing whale sounds suggest that the proposed method is comparable to the conventional SMS in both time and frequency domain. However, the proposed method outperforms the SMS in better spectrum matching.