Could Your Control Loops Be Telling You More?

PRONETIQS SID Webinar

September 23, 2026 | 10:00 AM MT / 12:00 PM ET

Your process can still be holding setpoint while the equipment behind it is beginning to behave differently.

That is because a controller does what it is designed to do: compensate.

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But changes in the relationship between setpoint, process value and control output can contain useful information about how a machine is responding—and whether that response is changing over time.

Join Streamline Process Management for an introduction to PRONETIQS SID and see how existing control-system data can be used to identify developing equipment issues before they become obvious.

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What We’ll Explore

During the webinar, we’ll look at:

  • How control-loop behaviour can provide an early indication that equipment response is changing.
  • Why a process can appear stable while the controller is working differently to keep it there.
  • How SID uses existing PLC and control-system data rather than requiring additional condition-monitoring sensors.
  • What kinds of assets and applications are well suited to control-behaviour monitoring.
  • How maintenance and reliability teams can use earlier diagnostic information to prioritize investigation and intervention.
  • Where this approach fits alongside existing predictive-maintenance and condition-monitoring strategies.

The process may still be producing. That does not mean the equipment is still behaving normally.

Who Should Attend?

This session is particularly relevant for people working with:

  • PLCs and industrial control systems
  • PID and other automated control loops
  • Maintenance and reliability
  • Process and controls engineering
  • Industrial automation
  • Operations and asset performance

You’ll get the most from the session if your team understands the control application and has practical access to the relevant PLC or control-system data.

Why Look at the Data You Already Have?

Predictive maintenance often starts with a question about what additional sensors, analytics or infrastructure should be added.

SID starts somewhere else:

What is the control system already telling us?

A controller continuously works to keep a process on target. If a mechanical or thermal change causes the equipment to respond differently, the controller may compensate before a traditional process alarm is triggered.

SID is designed to monitor those changes in control-loop behaviour and provide an earlier reason to investigate.

It is a purpose-built diagnostic approach that uses information already being generated by the automation system.

What Is PRONETIQS SID?

PRONETIQS SID is predictive-maintenance software designed to monitor changes in machine response through existing control-loop data.

It analyzes the relationship among:

Setpoint — what the process needs to achieve
Process value — what is actually happening
Control output — what the controller is doing to close the gap

By monitoring how that relationship changes over time, SID can identify deviations that may indicate developing mechanical deterioration.

SID operates on premises and does not require additional condition-monitoring sensors or a months-long custom data-science project.

PRONETIQS SID 5 Baliso

PRONETIQS SID provides visibility into monitored control loops, asset health and active alerts.

Is SID Worth Exploring for Your Application?

A strong starting point generally includes:

  • Automated equipment with meaningful control loops
  • Someone who understands the control application
  • Access to the relevant PLC or control-system data
  • Equipment where deterioration can change how the process responds
  • A meaningful business reason to identify that change earlier

If that sounds like your environment, the webinar will give you a practical introduction to how SID works and where it may fit.

Join Us

PRONETIQS SID Webinar
September 23, 2026
10:00 AM MT / 12:00 PM ET

register for the webinar

Have a specific application you’d like us to consider?

request an application review

Want the background first?

Read: Before AI, Ask What Your Control Loops Already Know

Or start with: When the Process Looks Fine but the Equipment Isn’t