Predictive Maintenance with MATLAB

Learn about condition monitoring and predictive maintenance algorithms

Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!

TechSource Systems Pte Ltd

Course
Highlights

This two-day course focuses on data analytics, signal processing, and machine learning techniques needed for predictive maintenance and condition monitoring workflows. Attendees will learn how to use MATLAB to import data, extract features, and estimate the condition and remaining useful life of equipment.

Topics include:

  • Importing and organizing data
  • Unsupervised anomaly detection
  • Creating supervised fault classification models
  • Preprocessing to improve data quality
  • Extracting time and frequency domain features
  • Estimating Remaining Useful Life (RUL)
  • Interactive workflows with apps
TechSource Systems Pte Ltd

Who Should
Attend

This course is intended for data scientists, engineers and managers who need to analyze signals (time series data) for data analytics and predictive maintenance applications.

TechSource Systems Pte Ltd

Course
Prerequisites

MATLAB Fundamentals

TechSource Systems Pte Ltd

Course
Benefits

Upon the completion of the course, the participants will be able to:

  • organize data for Predictive Maintenance applications
  • create visualization of machine features and condition indicators
  • create classification and regression models for anomaly detection and prediction
  • preprocess data to improve data quality and extract time and frequency domain features
  • estimate remaining useful life (RUL)
  • use diagnostic feature designer

Partners

TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

TechSource Systems is MathWorks Authorised Reseller and Training Partner

Upcoming Program

  • Please keep me posted on the next schedule
  • Please contact me to arrange customized/ in-house training

Course Outline

Importing Data and Processing Data

Objective: Bring data into MATLAB and organize it for analysis, including handling missing values. Process raw imported data by extracting and manipulating portions of data.

  • Store data using MATLAB data types
  • Import with datastores
  • Process data with missing elements
  • Process big data with tall arrays
TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

Finding Natural Patterns in Data

Objective: Use unsupervised learning techniques to group observations based on a set of condition indicators and discover natural patterns in a data set.

  • Find natural clusters within data
  • Perform dimensionality reduction
  • Evaluate and interpret clusters within data

Building Classification Models

Objective: Use supervised learning techniques to perform predictive modelling for classification problems. Evaluate the accuracy of a predictive model.

  • Classify with the Classification Learner app
  • Train classification models from labeled data
  • Validate trained classification models
  • Improve performance with hyperparameter optimization
TechSource Systems Pte Ltd

Exploring and Analyzing Signals

Objective: Interactively explore and visualize signal processing features in data.

  • Import, visualize, and browse signals to gain insights
  • Make measurements on signals
  • Compare multiple signals in the time and frequency domains
  • Perform interactive spectral analysis
  • Extract regions of interest
  • Generate MATLAB scripts for automation
TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

Preprocessing Signals to Improve Data Set Quality and Generate Features

Objective: Interactively explore and visualize signal processing features in data.

  • Import, visualize, and browse signals to gain insights
  • Make measurements on signals
  • Compare multiple signals in the time and frequency domains
  • Perform interactive spectral analysis
  • Extract regions of interest
  • Generate MATLAB scripts for automation

Estimating Time to Failure

Objective: Explore data to identify features and train decision models to predict remaining useful life.

  • Select condition indicators
  • Use lifespan data to estimate remaining useful life using survival models
  • Use run-to-threshold data to estimate remaining useful life using degradation models
  • Use run-to-failure data to estimate remaining useful life using similarity models
TechSource Systems Pte Ltd
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