Predicting Remaining Useful Life With MATLAB

Overview

The remaining useful life (RUL) of a machine is the expected life or usage time remaining before the machine requires repair or replacement. Predicting remaining useful life from system data is a central goal of predictive-maintenance algorithms.

The term life time or usage time here refers to the life of the machine defined in terms of whatever quantity you use to measure system life. Units of life time can be quantities such as the distance travelled (miles), fuel consumed (gallons), repetition cycles performed, or time since the start of operation (days). Similarly, time evolution can mean the evolution of a value with any such quantity.

Typically, you estimate the RUL of a system by developing a model that can perform the estimation based upon the time evolution or statistical properties of condition indicator values.

 

Course Highlights

This two-day training provides introduction to RUL estimation in MATLAB environment using condition indicators. RUL estimation is done using RUL estimator model approach that is provided by Predictive Maintenance Toolbox from different types of measured system data.

These models are useful when you have historical data and information such as:

  • Run-to-failure histories of machines like the one you want to diagnose
  • A known threshold value of some condition indicator that indicates failure
  • Data about how much time or how much usage it took for similar machines to reach failure (life time)

Course Objectives

The aim of this training is to provide participants with the introduction to RUL estimation with Predictive Maintenance Toolbox using historical data.


Course Benefits

Upon the completion of the course, the participants will gain understanding on:

  • Overall capabilities of Predictive Maintenance Toolbox
  • Type of condition indicators
  • Choosing RUL estimator models

 

Who Must Attend
Engineers, scientist, managers who wish to learn to predict remaining useful life to identify and prevent failures before they occur so that unnecessary maintenance can be reduced, and unplanned downtime can be eliminated.

 

Prerequisites

  • Attended Comprehensive MATLAB or equivalent experience in using MATLAB
  • Have knowledge on basics of statistics, machine learning, signal processing

 

Course Outline

 Introduction to Predictive Maintenance Toolbox

  • Why is Predictive Maintenance Important?
  • Predictive Maintenance Algorithm Workflow

Introduction to Remaining Useful Life Prediction

  • General workflow for using RUL estimation model
  • Terminology
  • RUL Estimator models

Similarity Models

Similarity models base the RUL prediction of a test machine on known behavior of similar machines from a historical database.

  • Similarity modeling requirements
  • Hashed-feature similarity model
  • Pairwise similarity model
  • Residual similarity model

Degradation Models

Degradation models extrapolate past behavior to predict the future condition.

  • Degradation modeling requirements
  • Linear degradation model
  • Exponential degradation model

Survival Models

Survival analysis is a statistical method used to model time-to-event data.

  • Survival modeling requirements
  • Reliability survival model
  • Covariate survival model

Case Studies

  • Similarity-based remaining useful life estimation
  • Wind turbine high-speed bearing prognosis

  

 

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