High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit

This paper proposes an effective envelope analysis-based methodology for machinery condition monitoring and validates its efficacy by identifying bearing failures with 1-s acoustic emission (AE) signals sampled at 1 MHz. The proposed condition monitoring methodology of low-speed bearings consists of denoising to improve the signal-noise ratio of the acquired AE signal by employing a soft-thresholding technique with adaptively estimated positive and negative noise levels and an effective envelope analysis to detect the periodic impacts of the AE signals inherent in bearing defects by utilizing a 2-D visualization technique based on the improved residual frequency component-to-peak ratios. Despite the fact that the proposed method shows satisfactory performance for bearing condition monitoring, its computational complexity limits its use in real-time applications. To improve the performance and reduce the energy consumption of the proposed method, this paper proposes an efficient parallel implementation of the proposed method on a general-purpose graphics processing unit (GPGPU) by exploiting the memory hierarchy and the massive parallelism inherent in the proposed method. Experimental results indicate that the proposed GPGPU-based approach achieves an at least 68.9× speed improvement compared to the same sequential implementation on well-known Texas Instruments digital signal processors (TI DSPs). In addition, the proposed GPGPU approach reduces the energy consumption by at least 66% compared to TI DSPs.