GPU Accelerated Prognostics

George E. Gorospe Jr., Matthew J. Daigle, Shankar Sankararaman, Chetan S. Kulkarni, and Eley Ng
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_17_033.pdf3.73 MBSeptember 6, 2017 - 10:37am

Prognostic methods enable operators and maintainers to predict future performance for critical systems. However, these methods can be computationally expensive and should 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 general and 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.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
033
Page Count: 
7
Submission Keywords: 
GPU
algorithms
battery health algorithms
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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