Let us show you how we can help create smarter, more sustainable facilities.
We look forward to speaking with you.


Motor Faults – Six Months of R&D in Five Days

One of the great things about working at an innovative startup like Verdigris is the ability to move fast. Really fast. 

UX design for motor fault detection notification using motor current signature analysisMy colleagues proved just how fast they could move last week by running a five-day design sprint as part of Google Ventures' "sprint week". Based on the excellent book "Sprint" by Jake Knapp at GV, the product design sprint is all about designing and testing a user-centered product prototype in just five days. The sprint has five clear stages: Unpack everything you know about the problem; Sketch a range of potential solutions; Decide on an approach to test and storyboard the product; Prototype a low-fi mockup... at Stanford, we also called this an "MVP" – minimum viable product; and finally, Test your prototype with live customers in 1:1 interviews. 

Motor Fault Alerts

The five-day design sprint team for Google Ventures #sprintweekFor the design sprint, our fearless, five-person team, led by UX designer Liz Khoo, decided to "productize" a motor fault detection and alert system. The Verdigris Energy Tracker notifications platform already has the ability to notify facility managers and chief engineers about energy consumption problems in their building, in real time. Separately, Verdigris' proprietary sensors are collecting tons of high definition data for critical equipment in customers' facilities. The obvious next step was to combine these two into a real-tme alert. 

So the Verdigris AI completes what's called Motor Current Signature Analysis – MCSA. Our R&D team has spent the past six months learning how to identify common induction motor faults using only electrical data via MCSA. I was personally surprised to learn just how much you can see in a motor using only its electrical signature. For example, compare the two high resolution analyses of electrical frequency – on the left, a healthy motor, and on the right, a motor with a broken rotor bar.

 electrical frequency of a healthy motor using MCSA - motor current signature analysiselectrical frequency of a failing motor using MCSA - motor current signature analysis

The anomaly is easy to see in this example. Now that our R&D team had refined the algorithm to reliably detect these anomalies, it was time to design the notification to make it usable for our customers. 

I won't steal Liz's thunder: read all about the rest of the UX design process in her article on Medium

And if you'd like to see the final result, email us for a demo. We'd love your feedback too!