filter_train

Training data from a real-world degradation process involving the clogging of gas filters. The dataset includes right-censored lifetimes due to a preventive maintenance policy. Failure is defined when pressure differential exceeds 600 Pa.

Provides tools for estimating the Remaining Useful Life (RUL) of degrading systems using linear mixed-effects models and creating a health index. It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) <doi:10.1109/TASE.2014.2349733>.

Pedro Abraham Montoya Calzada

degradr

Estimating Remaining Useful Life with Linear Mixed Effects
Models

Rogelio Salinas Gutiérrez

Silvia Rodríguez-Narciso

Netzahualcóyotl Castañeda-Leyva

filter_train function

A data frame with 49 degradation trajectories. Variables include:<dl>
 <dt>Time</dt>
<dd>Time</dd> <dt>Differential_pressure</dt>
<dd>Differential pressure across the filter in Pascal (Pa).</dd> <dt>Data_No</dt>
<dd>Unique identifier for each filter test unit. Integer from 1 to 50.</dd>
</dl>

Format

Training Data for Filter Degradation and RUL Prediction — filter_train

A data frame with 49 degradation trajectories. Variables include:<dl>
 <dt>Time</dt>
<dd>Time</dd>

 <dt>Differential_pressure</dt>
<dd>Differential pressure across the filter in Pascal (Pa).</dd>

 <dt>Data_No</dt>
<dd>Unique identifier for each filter test unit. Integer from 1 to 50.</dd>


</dl>

filter_train: Training Data for Filter Degradation and RUL Prediction

Description

Usage

Arguments

Format

References