Smart Maintenance: From Corrective to Predictive Using MATLAB and Simulink

Overview

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.

This way, you can minimize downtime and maximize equipment lifetime. In this overview session, you’ll learn:

• Workflow to develop a predictive maintenance algorithm
• Condition indicators, their importance and techniques to extract them from your data
• Applying machine learning models on the extracted condition indicators
• Understand different remaining useful life (RUL) estimator models
• Deploying these algorithms on edge/integrate with an enterprise system

About the Presenter

Amit Doshi, Sr. Application Engineer, MathWorks

Amit Doshi works as a senior application engineer at MathWorks in the area of technical computing. He is responsible for driving and managing the technology evaluation stage of the sales process. At MathWorks he focuses primarily on “data analytics”. 

Amit has over 13 years of experience working across industry. Over the years he has worked on data analytics, experimental test setup development, workflow automation, and system simulations. He previously worked at Suzlon Energy Limited in Pune and Germany, Texas Instruments in Germany, and IIT Bombay.

Amit holds a Bachelor’s degree in Mechanical engineering and a Master’s degree in Mechatronics.