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Predicting Remaining Useful Life using IoT - Adarsh Narasimhamurthy (MathWorks)

Predictive maintenance (PM) enables timely scheduling of maintenance by tracking the condition of a machine. Traditionally, you implement a PM system by manually collecting data stored locally on the machines, and analyzing it at a remote location. This requires significant time and resources. With the advent of IoT, you can create PM applications for near real-time system monitoring. Analytics running on the cloud estimate complex system characteristics to predict remaining useful life and generate alerts.

ThingSpeak is an IoT Analytics platform from MathWorks, makers of MATLAB. It enables you to collect data from your devices in real-time and rapidly prototype online PM applications. Most importantly, ThingSpeak removes the burden of standing up an IoT infrastructure and lets you focus on the algorithmic side of the problem. ThingSpeak analytics also provides MATLAB in the cloud.

Come, learn more about ThingSpeak and how you can benefit from using it for your IoT application.


Adarsh Narasimhamurthy is a senior engineer at MathWorks whose main area of focus is IoT analytics. He has worked in all of the key IoT domains including hardware connectivity, cloud platform for data collection & analysis, and desktop applications for exploratory analytics. He holds a PhD in Electrical Engineering from Arizona State University. He is a coauthor of the book titled “OFDM Systems for Wireless Communications” and has written several articles on analyzing IoT data.