# Health Indicator Designer

Interactively transform a set of features into a single composite health indicator that can be used to predict the remaining useful life (RUL) of a machine

*Since R2024a*

## Description

The Health Indicator Designer app allows you to fuse a set of features into a single health indicator (HI) that represents the state of health for the overall system. You can use this single indicator to simplify condition monitoring and RUL predictions in RUL applications.

The app generates MATLAB^{®} code that encapsulates the construction of the HI, which has the general form of
a scalar linear regression equation.

*H*(*t*) = *a*_{0} +
*a*_{1}*f*_{1}(*t*)
+
*a*_{2}*f*_{2}(*t*)
+ … +
*a*_{n}*f*_{n}(*t*)

Here, the *a _{i}* terms are the coefficients that
weight the feature contributions. The

*f*terms are the values of the individual features at time

_{i}*t*.

*a*

_{0}is the intercept value.

*H*is typically normalized to a range of [0,1] or [1, 0].

To use the app, you import a feature table that contains an ordered set of features that are computed from measured or simulated data, such as a feature table you export from Diagnostic Feature Designer. You also specify a target profile that represents the degradation profile that you expect the system to follow. The app provides parameters that you can adjust, and plots that show the fit results for the current set of parameters. The various parameters let you control the number of features that the HI incorporates and the mean squared error (MSE) of the fit of the HI to the target.

The goal is to design an HI that incorporates the fewest features possible to achieve the error performance that you require.

For more information on the app parameters and the plot contents, see the corresponding items in the Parameters section.

For information on the algorithms that the app uses, see Algorithms.

## Open the Health Indicator Designer App

MATLAB toolstrip: On the

**Apps**tab, under**Control System Design and Analysis**, click the app icon.MATLAB command prompt: Enter

`healthIndicatorDesigner`

.

## Examples

### Related Examples

## Parameters

## Programmatic Use

## Algorithms

Health Indicator Designer uses the elastic net method, which is related to the lasso algorithm (least absolute shrinkage and selection operator), from the Statistics and Machine Learning Toolbox™ to fit a model to the target. Elastic net regularization is a popular approach for model reduction, as it balances mean squared error and uncertainty.

The parameters and plots that the app displays are products of the `lasso`

processing, which the app configures for the elastic net option. A key
lasso tuning parameter is the regularization parameter *λ*, which influences
how many predictors or, for Health Indicator Designer, features, to use in the
model. The elastic net algorithm uses both *λ* and a second parameter,
*α*, which, for elastic net, is constrained between 0 and 1. Using
*α* has benefits especially when working with highly correlated predictors.
In the app, the **Feature density** slider represents
*α*.

For more information about the lasso and elastic net algorithms, see Lasso and Elastic Net.

## References

[1] Zou, Hui, and Trevor Hastie.
“Regularization and Variable Selection Via the Elastic Net.” *Journal of the Royal
Statistical Society Series B: Statistical Methodology*, vol. 67, no. 2, Apr.
2005, pp. 301–20.

[2] Moradi, Morteza, et al.
“Intelligent Health Indicator Construction for Prognostics of Composite Structures Utilizing a
Semi-Supervised Deep Neural Network and SHM Data.” *Engineering Applications of
Artificial Intelligence*, vol. 117, Jan. 2023, p. 105502. DOI.org (Crossref),
https://doi.org/10.1016/j.engappai.2022.105502.

## Version History

**Introduced in R2024a**