Predictive maintenance lets you monitor equipment health to avoid future failures during operation. It uses predictive algorithms with data from equipment sensors to estimate when your equipment will fail. It also pinpoints the root cause of problems in your complex machinery and helps you identify which parts need to be repaired or replaced.
This way, you can minimize downtime and maximize equipment lifetime.
In this ebook, you will help you :
Get started with predictive maintenance algorithm development with MATLAB® by explaining the terminology and providing access to examples, tutorials, and trial software.
Section 1: Introduction to Predictive Maintenance with MATLAB
Learn how predictive maintenance differs from the strategies such as reactive and preventive maintenance. Walk through the predictive maintenance workflow steps such as acquiring and preprocessing data, feature extraction, and training machine learning models.
Section 2: Extracting Condition Indicators with MATLAB
Learn how to extract distinctive features from your data and train machine learning models with the extracted features for classifying different fault types.
Section 3: Estimating Remaining Useful Life with MATLAB
Learn the different estimation models to predict remaining useful life of your system: similarity, survival, and degradation. Find out which RUL model is suitable for your system based on the data and system information you have available.
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