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Predictive Maintenance with MATLAB
Data Analysis and Machine Learning
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!
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 Data and Processing Data
- Finding Natural Patterns in Data
- Building Classification Models
- Exploring and Analyzing Signals
- Preprocessing Signals to Improve Data Set Quality and Generate Features
- Estimating Time to Failure
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.
Course
Prerequisites
MATLAB Fundamentals
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
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
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
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
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
