AI and Sound Analytics Driving Value in Manufacturing Operations – Intel on AI – Episode 46

February 13th, 2020 |
Image for FaceBook

Share this post:
Facebook | Twitter | Google+ | LinkedIn | Pinterest | Reddit | Email
This post can be linked to directly with the following short URL:

The audio player code can be copied in different sizes:
144p, 240p, 360p, 480p, 540p, Other

The audio player code can be used without the image as follows:

This audio file can be linked to by copying the following URL:

Right/Ctrl-click to download the audio file.
Connected Social Media - iTunes | Spotify | Google | Stitcher | TuneIn | Twitter | RSS Feed | Email
Intel - iTunes | Spotify | RSS Feed | Email
Intel on AI - iTunes | Spotify | RSS Feed | Email

In this Intel on AI podcast episode: One of the biggest challenges manufacturing operations face when adopting digitalization and intelligence is the cost and complexity to instrument existing machines, connect them to a network, and deploy relevant software. This is especially costly with legacy equipment that is not enabled with the necessary sensors, intelligence, or ability to communicate with plant infrastructure. Anand Deshpande, the Founder and CEO of Asquared IoT (A2IoT), joins the Intel on AI podcast to talk about how the Equilips 4.0 solution from A2IoT enables businesses to measure overall equipment effectiveness and provide insight into manufacturing operations right from the site of measurement. He explains how Equilips 4.0 is a completely non-invasive and non-touch device that analyzes sounds from industrial machines, welders, and other operations to provide real time feedback on the health and functionality of these operations. Equilips runs on Intel architecture and performs all of the computing at the edge, eliminating the need for a network or cloud and enabling usage in remote and rugged environments. Anand talks about how Equilips is able to transform legacy machines into AI enabled smart operations and highlights how A2IoT worked with Intel to greatly increase their performance by utilizing Intel Distribution of Python, Intel Optimizations for TensorFlow and Intel MKL-DNN.

To learn more, visit:

Visit Intel AI Builders at:

Tags: , , , , , , , , , , , ,
Posted in: Artificial Intelligence, Audio Podcast, Intel, Intel on AI