update

Update posterior parameter distribution of degradation remaining useful life model

Syntax

update(mdl,data)

Description

update(mdl,data) updates the posterior estimate of the parameters of the degradation remaining useful life (RUL) model mdl using the latest degradation measurements in data.

Examples

Update Exponential Degradation Model in Real Time

Load training data, which is a degradation feature profile for a component.

load('expRealTime.mat')

For this example, assume that the training data is not historical data. When there is no historical data, you can update your degradation model in real time using observed data.

Create an exponential degradation model with the following settings:

• Arbitrary $\theta$ and $\beta$ prior distributions with large variances so that the model relies mostly on observed data

• Noise variance of 0.003

mdl = exponentialDegradationModel('Theta',1,'ThetaVariance',1e6,...
                                  'Beta',1,'BetaVariance',1e6,...
                                  'NoiseVariance',0.003);

Since there is no life time variable in the training data, create an arbitrary life time vector for fitting.

lifeTime = [1:length(expRealTime)];

Observe the degradation feature for 10 iterations. Update the degradation model after each iteration.

for i=1:10
    update(mdl,[lifeTime(i) expRealTime(i)])
end

After observing the model for some time, for example at a steady-state operating point, you can restart the model and save the current posterior distribution as a prior distribution.

restart(mdl,true)

View the updated prior distribution parameters.

mdl.Prior

ans = struct with fields:
            Theta: 2.3555
    ThetaVariance: 0.0058
             Beta: 0.0722
     BetaVariance: 3.6362e-05
              Rho: -0.8429

Input Arguments

mdl — Degradation RUL model

linearDegradationModel object | exponentialDegradationModel object

Degradation RUL model, specified as a linearDegradationModel object or an exponentialDegradationModel object. update updates the posterior estimates of the degradation model parameters based on the latest degradation feature measurements in data.

For a linearDegradationModel, the updated parameters are Theta and ThetaVariance.

For an exponentialDegradationModel, the updated parameters are Theta, ThetaVariance, Beta, BetaVariance, and Rho.

update also sets the following properties of mdl:

• InitialLifeTimeValue — The first time you call update, this property is set to the life time value in the first row of data.

• CurrentLifeTimeValue — Each time you call update, this property is set to the life time value in the last row of data.

• CurrentMeasurement — Each time you call update, this property is set to the feature measurement value in the last row of data.

data — Degradation feature measurements

two-column array | table object

Degradation feature measurements, specified as one of the following:

• Two-column array — The first column contains life time values and the second column contains the corresponding degradation feature measurement.

• table or timetable object that contains variables with names that match the LifeTimeVariable and DataVariables properties of mdl.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• This command supports code generation with MATLAB^® Coder™. Before generating code that uses an RUL model, you must save the model using saveRULModelForCoder. When updating the model at run time, it is also useful to store the model state using readState. For an example, see Generate Code That Preserves RUL Model State for System Restart.

Version History

Introduced in R2018a