Visual intelligence: Give process data a view of the plant

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Axis Communications’ Adam Wales argues that process operators should connect video with production data to investigate disruptions faster and make better-informed decisions while problems are developing.

From the control room, an operator can follow a process through its measured values and alarms. Understanding what is physically happening around the equipment may still require someone to go and look. When production is disrupted, that leaves a practical question: can the operator see enough to decide what should happen next?

Ensuring a positive answer to that question depends on process plants building visual context into their operational systems. If understanding a stoppage depends on accounts gathered after the shift, the opportunity to intervene may already have passed.

With 57% of UK manufacturing decision-makers surveyed identifying rising operational costs as a major challenge, time spent establishing what happened deserves attention alongside the disruption itself.

Connecting video data to production events can give operators and engineers a view of conditions that measured values alone may not explain. Visual intelligence should therefore be designed around the decisions people need to make when a process needs attention.

When process data leaves questions

Factories generate an enormous amount of data, but how much of it provides context? Only 16% of respondents said data from security technology was fully integrated into operational decision-making. For most respondents, that potentially useful source of information is only partly connected to the production workflow, if it is used there at all.

An alarm may pinpoint the second that a line slowed, but if the cause is not reflected in routinely measured values, engineers must begin a manual investigation. Comparing logs and speaking to the shift team may uncover the reason, but the process begins with the problem already having an impact.

Managing by hindsight may explain yesterday’s disruption, but it does not help operators intervene while the event is still developing. Visual intelligence can solve this.

Associate a machine alarm or production timestamp with vision-based sensors, however, and an operator can go straight to the relevant visual overview. If the event is still developing, that same view can show whether immediate intervention is required. Recorded video then preserves the lead-up for engineers investigating the underlying cause.

Analytics can make the response even faster. A camera might detect an object appearing where the process does not expect it and send a time-stamped alert, with the relevant visual context, to an operator.

Video at work on the line

Manufacturers are already putting this approach into practice. BMW Group, for example, uses network cameras within its AIQX platform for automated quality inspection. Cameras along the assembly line capture detailed vehicle images, synchronised with their location in the plant. AIQX analyses them for defects and assembly errors, allowing staff to address identified problems before the line moves on.

Nestlé uses compact cameras to supervise robots within coffee-jar filling operations at its French production sites. When an incident occurs, teams can examine the visual context to diagnose the cause and refine machine settings. Cameras also give operators a live view of equipment in sensitive production areas, reducing unnecessary entry.

In both cases, visual context has become part of how the plant operates. Vision-based sensor output directly feeds into a quality or production workflow instead of simply remaining as evidence for a later investigation.

With the right integration, a visual detection could prompt an operator to intervene, trigger an inspection or even create a maintenance work order. The camera’s output becomes part of the plant’s normal response to a production problem.

Specify the view operators need

Existing security cameras may provide a starting point, but each view must be assessed against the operational question. The first step is to ensure visual sensors are detecting the right thing. Angle, field of view, resolution and frame rate must match the task. Lighting, vibration, dust or steam may also affect what can be seen.

The processing architecture should also follow the application. Edge analytics may suit a defined condition requiring a fast local response. Server- or cloud-based processing may be preferable where the application needs greater computing capacity or information from several sources.

Whichever approach is chosen, there also needs to be a clear process for responding. Who receives the alert? What do they see? And what action can they take? Open platforms allow camera events and metadata to pass into compatible industrial and maintenance systems, enabling deep, customised integrations.

Health and safety requirements demonstrate the need for that specificity. If the aim is to protect people in restricted areas, assure safe processes or identify missing personal protective equipment, the visual sensor needs a clear view of the relevant zone, and its analytics must be configured for that condition. The resulting alert should reach a supervisor who can verify it and respond, giving the system a defined role in areas that one person cannot watch continuously.

This is about protecting them and assuring processes in a way that supports proactive safety. That distinction must be designed into the project from the start. Connected cameras need appropriate access controls, software maintenance and lifecycle management. The purpose and retention rules for archived visual context should be clear, and worker privacy should be treated as a core design requirement rather than an afterthought.

Privacy masking can preserve a view of the process while protecting workers, or be used in reverse to protect the privacy of the environment. Early consultation can prevent an operational project being mistaken for personal surveillance and help build confidence that visual intelligence is being used to improve safety, resilience and operational understanding.

Test it against a recurring disruption

A recurring process problem provides a manageable starting point. Plant leaders should begin the integration testing process by selecting a common disruption whose cause is difficult to establish, then record how often it occurs and how much time teams spend investigating it.

The pilot should give the operator or engineer the view needed to make a specific decision, with analytics and integration supporting that response. Comparing the results with the previously established baseline will show whether investigation times and disruption have reduced. The pilot is also an opportunity to test image quality, alert handling and system links before the plant commits to wider deployment.

Before approving that expansion, plant leaders should ask the people handling the disruption what they could decide sooner, and whether that changed the outcome. Their answer should guide the next investment: a useful camera view earns its place in the process when it helps someone take the right action in time.

In Focus: A Spotlight on Manufacturing by Axis explores how visual intelligence can help manufacturers add operational context, close the visibility gap and support smarter, safer and more efficient manufacturing.

Adam Wales – Key Account Manager, End Customer, Axis Communications

Adam serves as the Axis Key Account Manager for the UK & Ireland, overseeing the company’s End Customer Digital Transformation initiatives in the area. With over two decades of experience in the electronic security sector, he has held diverse positions such as engineer, project manager, and enterprise account manager. Adam is driven by a keen interest in emerging technologies, adopting a solution-focused perspective and a professional, consultative method to address client needs.