nasa / prog_models

The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.
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Update tutorial #561

Closed teubert closed 1 year ago

teubert commented 1 year ago

Updated tutorial to include additional detail

github-actions[bot] commented 1 year ago

Thank you for opening this PR. Since this is a release branch, the PR must complete the release checklist, below:

github-actions[bot] commented 1 year ago
Benchmarking Results From: Test Time (s)
import main 0.17156890000000002
import thrown object 0.6117459000000001
model initialization 0.18187120000000023
set noise 0.8150787999999998
simulate 0.5600733999999998
simulate with saving 1.5984413000000002
simulate with saving, dt 2.0486250999999998
simulate with printing results, dt 2.5828469
Plot results 17.194283399999996
Metrics 0.04375780000000162
Surrogate Model Generation 2.5062524999999987
surrogate sim 1.6624014000000038
surrogate sim, dt 4.316143500000003
To: Test Time (s)
import main 0.1672511000000001
import thrown object 0.6218089
model initialization 0.1798343
set noise 0.8253720000000002
simulate 0.5628143000000003
simulate with saving 1.6183243000000003
simulate with saving, dt 2.073231
simulate with printing results, dt 2.5945941
Plot results 17.1926326
Metrics 0.04443780000000075
Surrogate Model Generation 2.487051600000001
surrogate sim 1.6602583000000024
surrogate sim, dt 4.283528199999999
github-actions[bot] commented 1 year ago
Benchmarking Results [Update] From: Test Time (s)
import main 0.1829727000000001
import thrown object 0.6535170000000001
model initialization 0.19044470000000002
set noise 0.8313253999999999
simulate 0.5910138000000003
simulate with saving 1.6831187
simulate with saving, dt 2.1638254000000003
simulate with printing results, dt 2.6917343999999996
Plot results 17.8026903
Metrics 0.04621960000000058
Surrogate Model Generation 2.587121400000001
surrogate sim 1.7116874000000024
surrogate sim, dt 4.482857899999999

To:

Test Time (s)
import main 0.17610130000000002
import thrown object 0.6416639
model initialization 0.19065029999999972
set noise 0.8363648000000001
simulate 0.5941984000000002
simulate with saving 1.6762681000000006
simulate with saving, dt 2.1526722000000005
simulate with printing results, dt 2.6774018
Plot results 17.780684100000002
Metrics 0.04554839999999771
Surrogate Model Generation 2.5851802
surrogate sim 1.7543975999999972
surrogate sim, dt 4.427645599999998
github-actions[bot] commented 1 year ago
Benchmarking Results [Update] From: Test Time (s)
import main 0.1084229000000001
import thrown object 0.4985465
model initialization 0.1557176
set noise 0.6909516000000002
simulate 0.49738250000000006
simulate with saving 1.3953817000000002
simulate with saving, dt 1.8167074000000003
simulate with printing results, dt 2.2759883
Plot results 14.1050767
Metrics 0.04283459999999906
Surrogate Model Generation 2.164301899999998
surrogate sim 1.3617227000000014
surrogate sim, dt 3.6870477999999984

To:

Test Time (s)
import main 0.1103326
import thrown object 0.4987254000000001
model initialization 0.1576362
set noise 0.6888554
simulate 0.5078099000000003
simulate with saving 1.4162122
simulate with saving, dt 1.8293906
simulate with printing results, dt 2.2878213
Plot results 14.038961400000002
Metrics 0.04318699999999964
Surrogate Model Generation 2.1948168000000017
surrogate sim 1.3728230000000003
surrogate sim, dt 3.7269701000000026