OEMs Get Smarter with Machine Control System Design

Smart objects can help OEMs ease integration of smart machines and improve the solutions they deliver

As an OEM, you are challenged to differentiate yourself amidst global competition and rapidly evolving technology. Smart machines and equipment provide unprecedented access to data, greater connectivity, and robust security – positioning you and your customers for greater success.

The demand for smarter, IoT-enabled machines is intensifying but integrating smart machines at the end-user site quickly and cost-effectively is a key challenge for many OEMs. The commonly asked question that must be addressed within the marketplace is: How can we make control systems even more information-enabled to ease integration – and improve the solutions smart machines deliver?

Smart objects for smarter machines

Innovative control system design tools currently in development are focused on changing the machine integration equation by allowing OEMs to define discoverable data points and the data relationship model well before system integration. These data points can now be defined at the control level as part of the machine programming process, creating a new level of efficiency in the design process.

At the core of this new functionality are system design instructions that enable you to configure ‘smart objects’ that become part of the tag structure. In line with information system application requirements, smart objects identify what data to collect – and how and when that data is collected.

A second set of instructions creates the organization around the data and determines how data is grouped. For example, if six data points are meaningful together, the instruction defines a single ‘parent’ to collect the data points synchronously.

Information gateway software – added to the machine control platform – finds the collected data along with the organizational model and maps it to a database automatically. To retrieve the needed data, information system applications communicate with the gateway where the collected data and the data model in accessible in database form.

New approach streamlines integration

This new approach to smart machine control system design streamlines one of the most arduous parts of the integration process – before your machine reaches the plant floor. It does so by leveraging the existing skill sets of your staff who are comfortable using PLC instructions to configure functionality in the control platform.

While streamlining integration is a critical goal, we envision that an approach based on smart objects will deliver additional benefits throughout the lifecycle of a machine, including:

Better, synchronous data

• On plant floors today, information applications poll machine control systems for individual data points based on intervals of time. Relationship models built with this asynchronous data are often incomplete, depending on the sequence of variables collected – and when a condition requiring analysis is discovered.
• Smart objects automatically trigger data collection based on conditions detected in the control system. All data related to the condition is collected synchronously when the event occurs – and delivered to the information gateway.
• In the short term, synchronous data is optimal for pinpointing the reasons for a machine state change from ‘run’ to ‘fault’, for example. In the longer term, synchronous data provides better insight into ways to improve Overall Equipment Effectiveness (OEE).

Smaller datasets requiring less processing power

• Different information applications often look for the same machine data. Today, each application typically polls the machine control system, creates a dataset – and expands the plant data footprint.
• Smart objects minimize interrogations by multiple applications often looking for the same thing. The result is significantly fewer datasets, which translate to lower costs related to data management, analysis and storage. In addition, the synchronous collection of data means less false entries, or ‘noise’, in the datasets and thus less processing power dedicated to identifying the noise and removing it before data is used in information system applications.

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