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Google Cloud and Litmus Co-Develop Data Connectivity Platform

New platform unifies factory data connectivity with cloud analytics and artificial intelligence for smart manufacturing.

Litmus Google

If there’s one thing industry’s embrace of cloud and edge computing technologies has shown us, it’s that hybrid systems—using both cloud and edge computing to handle data storage and analytics—are the preferred approach. As revealed in a 2019 Automation World study, digital transformation initiatives tend to follow a typical rollout pattern: Phase one is usually centered around the cloud to host core enterprise analytics applications to evaluate plant performance or for asset optimization to reduce production downtime. From there, manufacturers tend to invest in edge computing technologies for detailed onsite analytics delivered in near real time. The cloud also figures in as a means of providing additional storage and compute scalability.

This hybrid reality of cloud and edge computing can be seen in the development of the Manufacturing Connect platform by Google Cloud and Litmus (a supplier of industrial edge computing technologies). The idea behind the Manufacturing Connect platform is to simplify the process of collecting data from disparate factory devices and exposing it to Google Cloud data and artificial intelligence (AI) applications.

Vatsal Shah, CEO of Litmus.Vatsal Shah, CEO of Litmus.According to Litmus, Manufacturing Connect is a factory edge platform designed to support more than 250 machine communication protocols. Data is structured and stored locally and then sent to Google Cloud for analysis. The platform can also reportedly deploy and manage AI and machine learning models at the edge for closed loop AI applications.

Features of the Manufacturing Connect platform include data collection and engineering, data visualizations and KPI (key performance indicator) dashboards, containerized application deployment, and machine learning model runtime. It also includes out-of-the-box integration with several analytics packages, such as Looker for BI and analytics, and Vertex AI for machine learning and AI.”


   This video covers the initial work to align Litmus edge and Google Cloud technologies.

Explaining how Litmus Edge prepares data for Google Cloud analytics, Vatsal Shah, Litmus CEO, says, “Using Litmus technology, we collect data from industrial systems, normalize that data, and add all of the OT (operations technology) data variables for context locally. We push that data into the Google Cloud environment using publish/subscribe while Google can add more contextualized data on top at the enterprise level, such as from MES or ERP systems.  The structured final data point is ready to use for analytics. All of this happens automatically but it is configurable if the user would like to change anything.”

Charlie Sheridan, technical director of industry solutions for manufacturing at Google Cloud.Charlie Sheridan, technical director of industry solutions for manufacturing at Google Cloud.Charlie Sheridan, technical director of industry solutions for manufacturing at Google Cloud, adds, “Manufacturing Connect provides data interoperability for all devices—regardless of type or brand—by generating a standardized JSON payload format for all data streams. In addition, Manufacturing Connect and Manufacturing Data Engine (the Google technology that provides for the common data model) share a common metadata model that supports integrated data contextualization at the edge and in the cloud.”

Sheridan points out that Manufacturing Connect can be used across the discrete and process manufacturing verticals, such as automotive, aviation, electronics, semiconductors, medical devices, pharmaceuticals, chemicals, plastics, food and beverage, and packaging and processing.

Describing how end users can apply Manufacturing Connect, Sheridan says, “Once data is centralized and harmonized by the Manufacturing Data Engine and Manufacturing Connect, it can then be used to create custom dashboards to visualize key data—from factory KPIs (key performance indicators) such as overall equipment effectiveness (OEE), to individual machine sensor data, allowing them to uncover new insights and opportunities throughout the factory. These insights can then be shared across the enterprise and with partners.”

Two specific types of applications noted by Sheridan are:

  • Machine-level anomaly detection via Manufacturing Connects’ use of Google Cloud’s Time Series Insights, which analyzes real-time machine and sensor data such as noise, vibration, or temperature.
  • Predictive maintenance to reduce downtime and maintenance cost. Manufacturers can use ML (machine learing) models and high-accuracy AI optimizations that are “deployable in weeks,” says Sheridan.

   Learn more about the publish/subscribe method of network communications.


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