Accelerating Envelope Analysis-Based Fault Diagnosis Using a General-Purpose Graphics Processing Unit

Reliable fault diagnosis in the bearings of an induction motor is of paramount importance for preventing unscheduled motor breakdowns and significant economic losses. This paper presents a fault diagnosis approach using a genetic algorithm and time-varying multi-resolution envelope analysis to select an optimal passband and the most discriminative fault components, respectively, in the acoustic emission signal from bearings. However, the computational complexity of the approach limits its use in real-time applications. To address that issue, this paper presents a general-purpose graphics processing unit (GPGPU)-based fault diagnosis methodology to accelerate the process via the optimal use of the GPGPU’s global and shared memory resources and parallel computing abilities. Experimental results show that the proposed GPGPU implementation is approximately 19 times faster and uses 570 % less energy than CPU implementation.