# A walking robot runs thermal and acoustic inspection rounds and turns what it finds into work orders (AB InBev, BMW, Nestlé Purina)

> A walking robot runs thermal and acoustic rounds: AB InBev Leuven found nearly 150 anomalies in six months and cut average repair time to 13 days.

- Canonical URL: https://aiagentsformanufacturing.com/use-cases/robot-thermal-acoustic-inspection-rounds-ab-inbev-bmw/
- Department: Maintenance (https://aiagentsformanufacturing.com/departments/maintenance/)
- Evidence level: Verified. Real deployment at a named or precisely described company, with a measured result from a primary source.
- Added: 2026-10-20
- Last updated: 2026-10-20
- Publisher: AI Agents for Manufacturing (https://aiagentsformanufacturing.com)

## Role

Maintenance (reliability and preventive maintenance: inspection rounds). Typical users: maintenance manager, reliability engineer, preventive maintenance technician, maintenance electrician, utilities or energy manager

## The problem

Inspection rounds are the first preventive task to slip. Walking every line to take thermal readings on motors, gearboxes and electrical panels, and listening for compressed-air or gas leaks, is repetitive work and there are rarely enough people to do it on schedule. Leaks are hard to hear over running machines, so some teams shut lines down to find them or bring in contractors with specialist equipment. A motor running hot or a gearbox low on oil goes unnoticed until it stops a packaging line, and problems found late wait months for a repair. At BMW Hams Hall, manual inspection could take a couple of hours per shift for some of the most experienced specialists.

## How AI helps

A four-legged robot (Boston Dynamics Spot) walks pre-programmed routes on its own while production runs, and stops at fixed inspection points. A thermal camera captures heat on motors, gearboxes and electrical parts; an acoustic imager (Fluke SV600) picks out the sound frequencies of compressed air or gas escaping under pressure, even through background noise; a 360° camera and LIDAR record the area. When the robot finds an anomaly it sends an alert to the maintenance and production managers and enters it in the system, so work orders are generated in advance from its rounds. At BMW the data feeds the plant's digital twin, where employees view each inspection and submit a maintenance request. Technicians spend their time on repairs instead of on the rounds.

## Step by step

1. List the rounds that are skipped or done late today (thermal checks, leak surveys, gauge readings), the points on each route, and what missed faults and leaks cost over the last year.
2. Start with one or two routes on running lines where hot spots and leaks are common; fix the inspection points and an alarm threshold per point with the reliability engineer.
3. Check safety and access with the plant: walkways, stairs, doors, zones closed to the robot, and who supervises it during the pilot.
4. Run the robot's rounds alongside the manual route for a few weeks and compare findings, misses and false alarms.
5. Send each confirmed anomaly to the CMMS as a work order with the image, location and severity, and agree on who acts and how fast.
6. Track anomalies found, time from detection to repair, avoided stops and estimated leak cost; add routes, points or sensors once the team trusts the alerts.

## Tools

- Boston Dynamics Spot with a thermal camera and a Fluke SV600 acoustic imager: used at AB InBev Leuven, BMW Hams Hall and Nestlé Purina
- Other options to evaluate: fixed thermal cameras and continuous acoustic or ultrasound sensors on critical assets, a handheld acoustic imager on a manual route, wheeled or rail-mounted inspection robots
- CMMS to receive the work orders: SAP PM, IBM Maximo, Fiix, MaintainX, UpKeep
- Any LLM (ChatGPT, Claude, Copilot, Gemini) with the prompt below, on a week of inspection readings, to rank the findings and draft work orders

## Result

AB InBev brewery in Leuven, Belgium (ten packaging lines, over 50,000 containers an hour): after a test run in 2022, Spot now performs 1,800 individual inspections a week; in its first six months it found nearly 150 anomalies, more than double what the team expected, and average repair time fell from a few months to 13 days. Finds include a faulty transport motor that would have stopped a packaging line for at least 6 hours, gearboxes low on oil, faulty motor fans and a very small ammonia leak. Most anomalies are compressed-air and gas leaks; the team estimates an average air leak at about $550 in wasted product and leaks of ammonia and other expensive gases at up to $15,000. BMW Group Plant Hams Hall (engine plant near Birmingham, England, robot acquired in 2023): six autonomous walks a day, routes set by order of importance; in the first weeks of acoustic inspections it found air leaks in unexpected places, and its readings fill inspection gaps the digital twin had revealed. Nestlé Purina: thermal inspections on non-critical equipment and acoustic compressed-air leak detection in thirty-plus missions on packaging lines, to give time back to technicians. All three are Boston Dynamics case studies (vendor-published).

As reported by the source; most figures are vendor or self-reported and not independently verified.

## Human review: cautions

- The robot finds; people confirm and repair. Every alert needs a confirming check before parts are ordered or a line is stopped.
- Gas leaks (ammonia, CO2) and electrical hot spots follow your safety procedures first; the robot is not a gas detector or a safety system.
- Nestlé Purina uses the robot on non-critical equipment; critical rotating assets usually also need continuous sensors between rounds.
- Leak costs at AB InBev are the team's own estimates, and all figures are published by the robot vendor.

## Try it yourself (prompt)

```text
You are a reliability engineer reviewing this week's inspection rounds.

I will paste: (1) the inspection readings [point id, asset, location, date, type (thermal / acoustic / visual), value (temperature, leak size or decibel level), threshold for that point, notes or image reference]; (2) the asset list with criticality and production impact [text or table]; (3) open work orders [asset, description, status]; (4) the next planned stops [dates, lines].

Do this:
1. List every reading above its threshold or flagged in the notes, with the point, asset and value quoted.
2. Rank them by production risk and safety risk (gas leaks, electrical hot spots first), and say which already have an open work order.
3. For each new finding, draft a work order: what to inspect to confirm, likely cause, suggested action, skills, and the deadline tied to the next planned stop where possible.
4. List compressed-air and gas leaks separately with location and size as recorded; do not estimate their cost yourself, list the inputs I need for my own calculation.
5. Flag points with no reading, repeated alarms or thresholds that look wrong.

Never downgrade a gas leak or electrical hot spot to save a stop: flag it for a person to decide.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.
```

## Related use cases

- [Predictive maintenance from vibration sensors, planned in the CMMS](https://aiagentsformanufacturing.com/use-cases/predictive-maintenance-vibration-sensors-cmms/)
- [Condition-monitoring alerts ranked by an expert so teams act on the right ones (Boise Cascade, Arbec, Kerry Group)](https://aiagentsformanufacturing.com/use-cases/prioritized-condition-monitoring-alerts-assetwatch/)
- [Agentic work-order drafting and repair-window scheduling](https://aiagentsformanufacturing.com/use-cases/agentic-work-order-drafting-and-repair-window-scheduling/)
- [AI reads technicians' free-text work orders and assigns the failure codes, so the CMMS shows what really fails (Janssen Pharmaceutica, oil and gas operator)](https://aiagentsformanufacturing.com/use-cases/ai-failure-codes-from-maintenance-work-order-text/)

## Sources

- https://bostondynamics.com/case-studies/energy-savings-predictive-maintenance-at-ab-inbevs-largest-european-brewery/
- https://bostondynamics.com/case-studies/spot-inspires-innovation-at-bmw-group-plant/
- https://bostondynamics.com/case-studies/digital-transformation-at-nestle-purina/

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