Hi Orel,
To use the Predictive Maintenance Toolbox in MATLAB to calculate and update the Remaining Useful Life (RUL) as data arrives with quadratic degradation of the condition indicator, you can follow these steps:
- Import or load the sensor data into MATLAB.
- Select the appropriate quadratic degradation model to describe the sensor data. For instance, if the data exhibits quadratic degradation, you can use a quadratic model such as Y = A + Bx + Cx^2, where 'Y' is the condition indicator, 'x' is the time or the usage from the beginning of the sensor readings, and 'A', 'B', and 'C' are coefficients that needs to be estimated.
- Use the 'fit' function to fit the quadratic model to the sensor data. This function can be used to estimate the model coefficients, their confidence intervals, and the quality of the model fit.
- Once the model is fitted to the sensor data, you can use it to predict the RUL using 'predictRUL'function.
- You can monitor the accuracy of the RUL predictions using performance metrics such as root mean square error (RMSE), mean absolute error (MAE), or mean absolute percentage error (MAPE). These measures will help you assess the quality of the RUL predictions and refine the model if necessary.
Hope it helps!
