COURSE OVERVIEW

 This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators. 

 

This course is designed to provide participants with a detailed and up-to-date overview of Predictive Maintenance with MATLAB. It covers the concepts, benefits and challenges of predictive maintenance; the MATLAB environment and interface including data types, variables, and basic operations in MATLAB; the data import, export and data visualization in MATLAB and basic scripting and functions in MATLAB; the MATLAB toolboxes relevant to predictive maintenance; the data cleaning and handling missing values, data transformation and normalization and signal processing concepts; the time-domain analysis in MATLAB and frequency-domain analysis in MATLAB; and the feature extraction from signals using MATLAB's signal processing toolbox. 

 

During this interactive course, participants will learn the machine learning concepts and algorithms, regression models in MATLAB and classification models in MATLAB; the time-series analysis and forecasting in MATLAB and model evaluation and performance metrics; the fault detection and anomaly detection in MATLAB, fault diagnostics and root cause analysis and generating synthetic data for training models; deploying MATLAB models for real-time monitoring and integrating MATLAB with external systems; and creating dashboards and reports in MATLAB.