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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Release v1.5

New model type: Direct Models. Direct Models directly correlate current state and future load to time of event, rather than state-transition models which simulate forward to calculate time of event
New model types that combine multiple models:
- EnsembleModel: combinations of multiple models of the same system where results are aggregated
- CompositeModel: combinations of models of different systems that are interdependent
  - New Model: Aircraft flight model
  - New DataModel: Polynomial Chaos Expansion (PCE) Direct Surrogate model
  - Initial transition of Input/State/OutputContainer and SimResult to use Pandas DataFrame. This release will bring the interface more in compliance with DataFrames. v1.6 will fully transition the classes to DataFrames.
  - Implemented new metrics that can be used when calculating error: Root Mean Square Error (RMSE), Maximum Error (MAX_E), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Dynamic Time Warping (DTW)
  - Error calculation metric can now be changed in estimate_params
  - Revamping integration:
  - New integration methods: RK4 and methods from scipy.integrate
  - Integration is now set at the model level. For continuous models the specified integration method will apply when calling next_state
  - 3.11 support
  - Various bug fixes and performance improvements

Benchmarking Results [Update] From:	Test	Time (s)
import main	0.1453238
import thrown object	0.5428119
model initialization	0.1328954
set noise	1.2166670999999998
simulate	0.3159099999999997
simulate with saving	0.9775789000000001
simulate with saving, dt	1.1370993999999994
simulate with printing results, dt	1.3549890000000007
Plot results	15.039806299999999
Metrics	0.037466099999999614
Surrogate Model Generation	3.3532139
surrogate sim	1.0858053000000005
surrogate sim, dt	2.7367600000000003

Benchmarking Results [Update] From:

Test

Time (s)

import main

0.1453238

import thrown object

0.5428119

model initialization

0.1328954

set noise

1.2166670999999998

simulate

0.3159099999999997

simulate with saving

0.9775789000000001

simulate with saving, dt

1.1370993999999994

simulate with printing results, dt

1.3549890000000007

Plot results

15.039806299999999

Metrics

0.037466099999999614

Surrogate Model Generation

3.3532139

surrogate sim

1.0858053000000005

surrogate sim, dt

2.7367600000000003

Test	Time (s)
import main	0.14650609999999986
import thrown object	0.5341227
model initialization	0.16322790000000031
set noise	0.7019053
simulate	0.5090080000000001
simulate with saving	1.4432725999999998
simulate with saving, dt	1.8483844999999999
simulate with printing results, dt	2.2911259
Plot results	15.0757482
Metrics	0.03834799999999916
Surrogate Model Generation	2.2039326999999993
surrogate sim	1.476753800000001
surrogate sim, dt	3.8301649000000033

Test

Time (s)

import main

0.14650609999999986

import thrown object

0.5341227

model initialization

0.16322790000000031

set noise

0.7019053

simulate

0.5090080000000001

simulate with saving

1.4432725999999998

simulate with saving, dt

1.8483844999999999

simulate with printing results, dt

2.2911259

Plot results

15.0757482

Metrics

0.03834799999999916

Surrogate Model Generation

2.2039326999999993

surrogate sim

1.476753800000001

surrogate sim, dt

3.8301649000000033

nasa / prog_models

Release v1.5 #557

Release v1.5