GPU Accelerated Prognostics

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George E. Gorospe Jr. Matthew J. Daigle Shankar Sankararaman Chetan S. Kulkarni Eley Ng

Abstract

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

How to Cite

Gorospe Jr., G. E., Daigle, M. J., Sankararaman, S., Kulkarni, C. S., & Ng, E. (2017). GPU Accelerated Prognostics. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2437
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Keywords

battery health algorithms, algorithms, GPU

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Section
Technical Papers

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