GPU Accelerated PrognosticsPrognostic 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.
Document ID
20170012211
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Gorospe, George E., Jr. (SGT, Inc. Moffett Field, CA, United States)
Daigle, Matthew J. (NIO San Jose, CA, United States)
Sankararaman, Shankar (SGT, Inc. Moffett Field, CA, United States)
Kulkarni, Chetan S. (SGT, Inc. Moffett Field, CA, United States)
Ng, Eley (Universities Space Research Association Moffett Field, CA, United States)
Date Acquired
December 18, 2017
Publication Date
October 2, 2017
Subject Category
Computer Systems
Report/Patent Number
ARC-E-DAA-TN46389
Meeting Information
Meeting: Annual Conference of the Prognostics and Health Management Society 2017