[ Success Story ]
Martin Schlaffer
Software Development
Software Development
2 Min
+ + + Automotive Testing Expo, Stuttgart, June 2026 – The story begins with a familiar sound: A coffee machine grinds beans, builds up pressure, activates valves, and brews an espresso. What seems like a brief moment at the exhibition booth is a complete engineering process on a small scale. Sensors measure vibrations, temperature, and, optionally, sound. An edge-to-cloud pipeline detects events, calculates key metrics, and displays the results on the dashboard. + + +
NVH-teams are familiar with signals, spectra, and levels. The next step comes when measurement data is no longer evaluated individually, but instead automatically processed through a reproducible data chain. This is exactly where Brewline comes in.
A coffee machine provides a simple yet technically plausible example: the grinder, pump, milk system, espresso, cappuccino, or rinse cycle all generate characteristic acoustic and vibration patte
. These processes are brief, repeatable, and immediately understandable. This makes the pipeline visible without having to explain it in abstract terms.
A coffee machine provides a simple yet technically plausible example: the grinder, pump, milk system, espresso, cappuccino, or rinse cycle all generate characteristic acoustic and vibration patte
. These processes are brief, repeatable, and immediately understandable. This makes the pipeline visible without having to explain it in abstract terms.
The demonstrator translates a technical data pipeline into a concrete NVH experience. What matters is not the coffee machine itself, but the path from the measurement signal to actionable information.
- Measurement data is collected and processed close to the source
- Continuous signals are converted into individual events
- Time-series data is used to generate key metrics, classes, trends, and anomalies
- The dashboard displays not only curves but also understandable results within the context of the process
This transforms an isolated measurement into a repeatable process. Multiple repetitions enable comparability, statistical analysis, and a reliable picture of the system’s condition.
Without a pipeline, the traditional workflow remains fragmented: Start the measurement, open the file, search for relevant time ranges, generate metrics, export the results, and recombine them for each iteration. This works for individual analyses. But when applied across many events, it becomes slow, inconsistent, and difficult to scale.
What matters is the system logic:
What matters is the system logic:
- Connect (each) data source
- Find and index (all) data
- Classify data and file formats
- Standardize / convert data
- Launch analysis and validation workflows
- Use results to inform engineering decisions
- Share and reuse data globally
The Brewline exemplifies how technical steps are integrated into an automated logic: The machine generates NVH signals. The pipeline identifies which process has taken place. The dashboard provides the time, class, key metrics, and trends. NVH expertise remains at the core of the process; the pipeline makes the repeatable steps faster, more consistent, and compatible with further process decisions.
Measurement systems generate data. Automated pipelines generate engineering intelligence.
Measurement systems generate data. Automated pipelines generate engineering intelligence.
The Brewline Demonstrator at ATE 2026
A pipeline is not a new measurement principle here. It is the automated workflow that defines what happens to the NVH data after it is captured – step by step. The measurement remains the same. The workflow after the measurement changes completely.
1. Measurement
2. Edge Capture
3. Analysis
4. AI-Driven Event Classification
5. Cloud & Dashboard
6. Decision
The pipeline transforms signals into events, events into information, and information into decisions.
1. Measurement
- Sensors measure vibrations, temperatures, and, optionally, sound from the coffee machine.
NVH meaning: Time-domain data from acceleration, vibration velocity, or sound pressure channels
2. Edge Capture
- Data is acquired and processed close to the measurement point.
NVH meaning: Triggering, segmentation, channel verification, and initial parameter values
3. Analysis
- Signals are converted into concise, comparable curves and numerical values.
NVH meaning: RMS, peak level, duration, spectral bands, and peak frequencies
4. AI-Driven Event Classification
- The pipeline classifies espresso, cappuccino, rinsing, or unknown events based on learned or predefined signal patterns.
NVH meaning: Classification based on typical signal and parameter patterns
5. Cloud & Dashboard
- Results are centrally processed, compared, and displayed in real time.
NVH meaning: Reproducible evaluation, statistics, trends, and optional reports
6. Decision
- Indications or process responses can be derived from the results.
NVH meaning: Condition monitoring, quality indicator, test decision, or further analysis
The pipeline transforms signals into events, events into information, and information into decisions.
The process is designed to be directly observable: A cappuccino or espresso is brewed; the system detects the vibration, identifies the event, and extracts key metrics. The dashboard displays the time, the identified process, relevant levels, key metrics, and—if configured—a brief report.
Over the course of multiple brews, these individual events are aggregated into statistics: count, classes, level distribution, trends, and anomalies. The question shifts from “What does this signal look like?” to “What is happening in the process, how often does it happen, and is the behavior changing?”
Over the course of multiple brews, these individual events are aggregated into statistics: count, classes, level distribution, trends, and anomalies. The question shifts from “What does this signal look like?” to “What is happening in the process, how often does it happen, and is the behavior changing?”
Brewline does not evaluate the taste of the coffee. The demonstrator shows how technical conditions and events can be detected and compared based on measurable vibration and noise patterns.
- Operating states are distinguished based on signal and parameter patterns
- Parameters such as peak level, RMS, spectral energy, or event duration are more reproducible than subjective impressions
- Event classes such as “Cappuccino detected” is plausible if the reference data is sufficiently discriminating
- Anomalies are initially understood as technical state or process indicators—not as blanket quality judgments
The Coffee Case demonstrates the practical benefits of an automated edge-to-cloud pipeline in three dimensions:
Efficiency
- Recurring analysis processes run automatically instead of manually
- Measurements can be evaluated immediately after data becomes available
- Individual events and long-term statistics are derived from the same database
Quality and Comparability
- Identical events are analyzed according to the same rules
- Manual misinterpretations and inconsistent evaluations are reduced
- New event classes can be added using reference data and analysis modules
Comprehensibility and Transferability
- The pipeline concept is illustrated using a familiar everyday process
- The logic can be applied to pumps, actuators, electric motors, end-of-line tests, and test benches
- The entry barrier remains low without sacrificing the technical depth of NVH.
End-to-End Data Pipeline From Edge Acquisition to AI-Based Classification
On Display at ATE
At ATE, Brewline demonstrated the complete workflow in action: edge-based NVH data acquisition, event detection, feature extraction, cloud orchestration, live dashboards, and optional event or series reports.
Beyond the exhibition, the same principle applies across domains: physical-dynamic data from any source is connected through a robust data chain, automatically analyzed, and transformed into actionable information.
Any Source. Any Link. One Cloud.
From Sensor to Decision.
Beyond the exhibition, the same principle applies across domains: physical-dynamic data from any source is connected through a robust data chain, automatically analyzed, and transformed into actionable information.
Any Source. Any Link. One Cloud.
From Sensor to Decision.
Conclusion
The Coffee Case brings automated edge-to-cloud workflows to life for NVH and engineering teams. Vibration and acoustic signals are transformed into metrics, event classes, and trends through a repeatable, comparable, and scalable process.
More than just a trade show demonstrator, Brewline illustrates the path from measurement data to Engineering Intelligence – accessible to first-time audiences and relevant to experts in testing, validation, and data-driven engineering.
More than just a trade show demonstrator, Brewline illustrates the path from measurement data to Engineering Intelligence – accessible to first-time audiences and relevant to experts in testing, validation, and data-driven engineering.
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Did you miss us at ATE 2026?
Talk to us about automating recurring NVH analyses in your measurement, test bench, or production environment – from data acquisition and processing pipelines to cloud-based dashboards and decision support.
Talk to us about automating recurring NVH analyses in your measurement, test bench, or production environment – from data acquisition and processing pipelines to cloud-based dashboards and decision support.
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