Reliable data enhances automation product designs

Analog measurements take many forms, but can be classified into two types-physical measurements and compositional measurements. The first type includes pressure, temperature, flow, force, vibration, mass, and density. The second includes measurements such as conductivity, pH, and chemical analysis. Ensuring accuracy in these measurements helps with automation-related product or system design.

The need to measure and control the operation of machinery or process equipment is as old as the Industrial Revolution. Plant instrumentation has become the nerves and brain of the modern manufacturing plant. It regulates and supervises the operation of the equipment within the plant. It also provides the means to make plants economically viable. Instrumentation allows the use of processes, which would be difficult or impossible to operate without automation.

Instruments have grown from purely analog systems to the smart sensors and systems in use today, ranging from simple potentiometers to complex analyzers such as infrared spectrophotometers. Yet, for all the advances in systems development, the world around us is analog and analog field measurements and the electronic signals that carry them are still necessary in all systems.

Analog measurement quality

Obtaining, maintaining, and improving analog measurement quality is the goal of proper signal conditioning and is a fundamental concern in automation product design that is often overlooked. To achieve optimal system performance and save time and resources, design teams need to rely on proven products from industry experts to front-end systems that interface to analog signals and to generate output signals, which control pumps, motors, and other machinery.

For factory automation markets, helpful products include signal conditioning, data acquisition, and data communication hazard protection products. Multiple generations of products and on-site manufacturing take advantage of shrinking discrete part geometries and increased system-on-a-chip (SoC) functionality to ensure innovative and cost-effective products can help customers with their product design challenges. 

Ensure reliable data

System reliability is essential to any successful automation product design. System component lifecycles, mean time between failure (MTBF), and long-term accuracy must be considered at the onset. Designs should be modular to allow ease of installation, ease of maintenance, upgrade for performance as technologies improve, and for replacement in the unfortunate but often occurrence of system component obsolescence. Choose established industry leaders with proven product reliability when sourcing system components.

The world is hungry for data. This is especially true for industrial applications, manufacturing, laboratories, and medical applications. Data analysis is used to:

• Automate processes and expand capabilities
• Explain the health of system components, efficiency of processes, and fault conditions.
• Control and analyze data for optimal performance and efficiency.

Preserving signal integrity in harsh industrial environments can add reliability to test and measurement applications. Removing analog problems from systems design and development can provide a better user experience for data acquisition and control.

John Lehman is engineering manager and Georg Haubner is sales and marketing manager at Dataforth Corp. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, mhoske@cfemedia.com.

KEYWORDS Signal integrity, data acquisition

Product designs are enhanced with reliable signals.

Accurate data augments product design.

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