Visual Daily Inspection

Overview

Daily visual inspection is the routine that keeps a plant running and, in most facilities, the routine that is least well instrumented. An operator walks a defined route with a clipboard or a tablet, looks at gauges, seals, belts, and couplings, notes anything abnormal, and files a report. The inspection happens; the evidence rarely survives in a form anyone can compare against last week.

Moving the inspection onto a private 5G network changes what the routine produces. The operator carries a 5G tablet or camera and captures images at defined inspection points. Those images upload immediately to an inference service on the Onyx Edge, which compares them against a reference and against the same point's history, and flags deviations before the operator has walked to the next point. Fixed cameras cover the points that are dangerous or awkward to reach. The output is no longer a checklist that was ticked: it is a dated, indexed visual record with automated deviation detection over it.

The network requirement is different in character from the other manufacturing workflow on this site. AGV control needs a 10 ms budget and modest bandwidth. Inspection needs a large, bursty uplink and tolerates a hundred milliseconds of latency comfortably. The two coexist on one network precisely because QoS keeps them apart.

How It Works

The capture loop

What each component does

Component	Role in this workflow
Operator device	Rugged 5G tablet or handheld camera, usable in gloves. Presents the capture guide so today's image is comparable to the reference and to history
Fixed cameras	At points that are hazardous, elevated, enclosed, or awkward to reach. Visual, and thermal where the failure mode is thermal
Asset tags	QR, barcode, or RFID at each point, binding a capture to the right asset unambiguously
Onyx RU	Coverage along the inspection route: plant rooms, gantries, outdoor yards. Areas Wi-Fi is rarely extended to
FHM / shared cell	An upload in progress is not interrupted as the operator walks between coverage areas
Onyx Edge · 5G Core	5QI 4 GBR: enough guaranteed uplink for a capture burst, without the priority that would let it disturb control traffic
Local breakout	Imagery reaches on-site inference and goes no further. Usually a precondition for the project being approvable at all
Inference service	GPU host on or beside the Onyx Edge. Scores each capture against the point's reference image and its history
CMMS / EAM	Where a confirmed deviation becomes a work order rather than a note
Onyx Portal	Per-device throughput and failed-upload diagnostics, per inspection round

The capture path, hop by hop

#	From → To	Interface	What happens
1	Operator → Inspection point	Asset tag scan	Position confirmed; the capture is bound to the correct asset
2	Device → capture	Camera + capture guide	Reference framing presented so the image is comparable to history
3	Device → Onyx RU	5G NR Uu, uplink	5–20 Mbps burst on 5QI 4; 2–15 MB per image, tagged with asset ID, timestamp, operator, conditions
4	RU → FHM → Onyx Edge	eCPRI, aggregated fronthaul	Upload continues uninterrupted as the operator moves
5	UPF → Inference service	N6 local breakout	Image reaches the GPU host on site. It does not leave the plant
6	Inside inference	Model comparison	Scored against the reference image and the point's own history
7	Result → Device	Downlink	Under five seconds: while the operator is still at the asset
8	Operator → decision	Confirm or dismiss	The operator adds the context the model cannot see
9	Confirmed → CMMS	API	Work order raised with the image attached
10	Dismissed → Inference	Feedback	Dismissals are the training signal that reduces false positives

The constraint is burst capacity, not latency

This workflow tolerates 100 ms comfortably: it is the most latency-relaxed page in this section. It does not tolerate fifty operators uploading 10 MB images in the same shift-change window. Size for the burst, and consider staggering routes across the shift.

Hop 7 is worth stating plainly as a design rule: it is a network requirement expressed as a UX requirement. If the result does not return in seconds, hop 8 does not happen: the operator walks on, the flag queues for later review, and the system loses the cheapest and most reliable filter it has between a real deviation and a shadow.

Network Requirements

Parameter	Target
Uplink during capture, still images	5–15 Mbps burst
Uplink during capture, short video or thermal sequence	10–20 Mbps burst
Per-image payload	2–15 MB depending on sensor and format
Aggregate uplink, 50 capture points at peak	50–200 Mbps
Fixed camera continuous uplink	3–8 Mbps each where streaming rather than capturing
One-way network latency	< 100 ms
Inference round trip, capture to result	< 5 s
Packet error rate	10-6: image integrity matters; this is an evidentiary record
Availability	99.9%
Primary QoS	5QI 4: GBR, 300 ms PDB, 10-6 PER
Alternative for scheduled bulk upload	5QI 72 or non-GBR 5QI 6
TDD pattern	Uplink-weighted, DDDSU or DDSUU
Bands	n77, n78, n48 (CBRS), n40

Deploying It

1

Define every inspection point

Asset ID, what is inspected, what normal looks like, and the capture geometry: position, angle, distance, lighting. Classify each as operator-captured or fixed-camera; hazardous, elevated, and confined-space points belong to fixed cameras.

2

Build the reference set

Capture each point under known-good conditions. Automated comparison is only as good as its reference. Agree the deviation classes to be detected: leak, corrosion, misalignment, wear, missing fastener, abnormal gauge, thermal anomaly, with plant maintenance before model deployment.

3

Configure the network and edge

Inspection subscriber group on 5QI 4 or 72; uplink-weighted TDD where it shares a carrier. Deploy the inference service on the Onyx Edge or a GPU host reached through local breakout.

4

Onboard devices

SIMs for tablets, handheld cameras, and fixed cameras. Load the route onto operator devices: point sequence, capture guidance, and the previous result for each point. Configure fixed-camera schedules and any lighting triggers.

5

Run the round

As described in the capture loop above, with the operator's confirmation happening at the asset, not at a desk afterwards.

6

Review and trend

Triage by severity and schedule against maintenance windows. Trend each point's history to catch the slow deviations a daily comparison misses: corrosion advancing a fraction a week, a belt creeping out of alignment over a month. Review model performance against operator confirmations and dismissals.

7

Retain as evidence

Keep the visual record with its metadata for the period the plant's regulatory or insurance regime requires. Produce completion reports: which points, when, by whom, and with what result.

Devices & Ecosystem

• 5G rugged tablets and handheld cameras: the operator's capture device, rated for the plant environment and usable in gloves.
• Fixed 5G cameras: at hazardous, elevated, or enclosed points; visual, and thermal where the failure mode is thermal.
• Thermal imagers: for bearings, motors, electrical connections, and steam systems, where the deviation is invisible in the visible spectrum.
• Edge inference platform: a GPU-equipped host running the comparison and anomaly models, on or beside the Onyx Edge.
• CMMS / EAM integration: so a confirmed deviation becomes a work order rather than a note.
• Asset tags: QR, barcode, or RFID at each point, to bind a capture to the right asset unambiguously.

Design Considerations

Capture consistency determines detection quality. The largest source of false positives is not the model: it is inconsistent framing and lighting. Invest in capture guides, fixed mounting points for the operator's device, and controlled lighting at points where ambient light varies through the day. A point captured from a different angle each day cannot be compared to itself.

Latency is not the constraint; burst capacity is. This workflow tolerates 100 ms comfortably. It does not tolerate an uplink that cannot absorb fifty operators uploading 10 MB images in the same shift-change window. Size for the burst, and consider staggering routes across the shift.

Keep the imagery on site. Inspection imagery is a detailed visual record of process equipment, tooling, and layout. Local breakout to on-site inference is frequently the reason the project is approvable at all. Confirm the retention and access policy before the first capture.

Design the human confirmation step in deliberately. An inspection system that files flags for later review loses the operator's context: the one thing that most cheaply separates a real deviation from a shadow. Return the inference result fast enough that confirmation happens at the asset. This is a network requirement expressed as a UX requirement.

Plan for the model's first months. Early false-positive rates will be high, and an operator who learns to dismiss everything has been trained out of the workflow. Start with a narrow set of high-confidence deviation classes, widen as the confirmation data accumulates, and show operators that their dismissals change the system's behavior.

Treat the record as evidentiary. If the inspection record may be used for regulatory, insurance, or incident-investigation purposes, the integrity requirements change: 10^-6 packet error rate, verified upload, immutable timestamping, and a retention policy agreed before deployment rather than after an incident.

Validation Checklist

• Every inspection point defined with asset ID, capture geometry, and normal condition
• Points classified as operator-captured or fixed-camera; hazardous points assigned to fixed cameras
• Reference image set captured under known-good conditions for every point
• Deviation classes agreed with plant maintenance before model deployment
• Coverage validated along the entire inspection route, including plant rooms and outdoor sections
• Peak burst test: full operator complement uploading simultaneously at shift change
• Inspection traffic on its own bearer, verified not to affect control-traffic latency
• Capture-to-result round trip measured and under five seconds at the furthest point on the route
• Upload integrity verified; failed uploads retried and surfaced, never silently dropped
• Imagery confirmed to remain on site; retention and access policy documented
• Operator confirmation step tested in the field, in gloves, in plant lighting
• CMMS integration verified end to end: confirmed deviation produces a work order with image attached
• False-positive rate baselined, with a review cadence agreed
• Fixed-camera schedules and lighting triggers validated across a full day/night cycle

Next steps

• Size it. Take these requirements through Plan Your Network: capacity, coverage, architecture, and a preliminary bill of materials.
• Compare it. Network Requirements at a Glance puts every workflow side by side, for sites carrying more than one.
• Talk to us. For a validated design and a quote, contact GXC.

Related reading

• AGV & AMR Fleet Control on 5G
• Drone Site Inspection on 5G
• Network Requirements at a Glance