Predictive Maintenance with MATLAB

Who Should Attend?

Data Scientist, engineers and managers who need to analyse signals (time series data) for data analytics and predictive maintenance applications

Duration: 3 Days

Focuses on data analytic, signal processing, and machine learning techniques needed for predictive maintenance and condition monitoring workflows. Attendees will learn about importing, preprocessing, organizing data. Using signal processing techniques to extract time-frequency information. Estimate Remaining Useful Life (RUL) and apply machine learning techniques.

Topics include:

  • Creating, importing and visualizing signals
  • Preprocessing to improve data quality, including filling data gaps, resampling, smoothing, aligning signals, finding and removing outliers, and handling non-uniformly sampled signals
  • Extracting features in the time and frequency domains, including finding signals from patterns, finding change points, locating peaks, and identifying trends
  • Organizing and preprocessing data
  • Clustering data
  • Creating classification and regression models
  • Interpreting and evaluating models
  • Simplifying data sets
  • Identify features and train decision models to predict remaining useful life (RUL).
  • Import and organize data
  • Preprocess time-based signals and extract key features in the time and frequency domains
  • Build classification and regression model using Statistic and Machine Learning toolbox
  • Ensemble data and train model to predict remaining useful life (RUL) with Predictive Maintenance Toolbox

DAY 1 of 3: Signal Preprocessing and Feature Extraction for Data Analytics with MATLAB

Explore and Analyze Signals (Time Series) in MATLAB

Objectives: Learn to easily import and visualize multiple signals or time series data sets to gain insights into the features and trends in the data.

  • Import, visualize, and browse signals to gain insights
  • Make measurements on signals
  • Compare multiple signals in the time and frequency domain
  • Perform interactive spectral analysis
  • Extract regions of interest for focused analysis
  • Recreate analysis with auto-generated MATLAB scripts

Preprocess Signals to Improve Data Set Quality

Objectives: Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps.

  • Perform resampling to ensure common time base across signals
  • Work with non-uniformly sampled data
  • Find gaps in data and remove or fill gaps
  • Remove noise and unwanted frequency content
  • Perform wavelet denoising
  • Use the envelope spectrum to perform fault analysis
  • Locate outlier values in data and replace them with acceptable data
  • Locate signal changepoints and use boundaries to automatically create signal segments

Extract Features from Signals

Objectives: Apply different techniques in time and frequency domains to extract features. Become familiar with the spectral analysis tools in MATLAB and explore ways to bring out features for multiple signals.

  • Locate peaks
  • Locate desired signals from patterns in the time and spectral domains
  • Use spectral analysis to extract features from signals
  • Perform classification using supervised learning
  • Use the Classification Learner app to interactively train and evaluate neural networks

Day 2 of 3: Machine Learning and Predictive Maintenance with MATLAB

Importing and Organizing Data

Objectives: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.

  • Data types
  • Tables
  • Categorical data
  • Data preparation

Finding Natural Patterns in Data

Objectives: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

  • Unsupervised learning
  • Clustering methods
  • Cluster evaluation and interpretation

Building Classification Models

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

  • Supervised learning
  • Training and validation
  • Classification methods

DAY 3 of 3: Machine Learning and Predictive Maintenance with MATLAB

Improving Predictive Models

Objectives: Reduce the dimensionality of a data set. Improve and simplify machine learning models.

  • Cross validation
  • Hyperparameter optimization
  • Feature transformation
  • Feature selection
  • Ensemble learning

Building Regression Models

Objectives: Use supervised learning techniques to perform predictive modelling for continuous response variables.

  • Parametric regression methods
  • Nonparametric regression methods
  • Evaluation of regression models

Estimating Time to Failure

Objectives: Explore data to identify features and train decision models to predict remaining useful life (RUL).

  • Data Organization & Labeling using Data Ensembles
  • Condition Indicator Design
  • Remaining Useful Life (RUL) Estimator Models
  • RUL Estimation Using Dynamic Models

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