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SCIENCE PASSION TECHNOLOGY

Progressive Visual Segmentation of
Vibration Signals to Detect Change Points

Julian Rakuschek, Johanna Schmidt, Udo Schlegel, Michèle Posch, Jutta Isopp, Tobias Schreck

VMV 2026 - 25.09.2026

All about vibrations

Engine

+

Sensor

=

Vibration signal

Why analyze vibrations?

Find long-term deviations

Why analyze vibrations?

Find long-term deviations

Why analyze vibrations?

Find anomalies (short-term, unexpected)

Why analyze vibrations?

Find anomalies (short-term, unexpected)

Why analyze vibrations?

Predict faults

Why analyze vibrations?

Predict faults

A good segmentation can help solve these tasks!

Condition Monitoring

Predictive Maintenance

Anomaly Detection

The transitions between segments mark change points

This is hard to read

We transform it to the frequency domain

The big problem with vibrations:
Large data volumes

High sampling rate = large data volumes

Example: 30 kHz (30 000 samples per second)

Assumption: 8 byte per sample

After one minute: 14,4 MB
After one hour: 864 MB
After one day: 20,7 GB
After a year: 7,5 TB

How do domain experts approach this?

Signal Analysis via Manual Window Positioning

How about 10TB?

Manual scanning is tedious!

Our Method: Sampling

Our approach: Sampling

We iteratively construct an overview

In many cases a small fraction of the data suffices!

Do we need to save the full signal?

No!

We only save a FFT histogram per sample into the database.

Further representations possible

Time Series Projections

We start with an empty timeline

Next we randomly draw samples

The subsequences are grouped by similarity

The background is colored by closest label

The height is adjusted based on the density

For a subset of samples we can show representations as thumbnails.

We obtain a segmentation

With more samples, the segmentation becomes more accurate.

5 samples

10 samples

25 samples

50 samples

How can users steer the sampling?

Defining intervals to constrain the sampling

(Accelerated video footage)

How can users explore the result?

Classic zooming and panning

Inspecting samples in detail

Other sampling strategies

Binary Sampling (1)

Binary Sampling (2)

The result: Sharp boundaries

Gap Filling (1)

Gap Filling (2)

The result is a uniform distribution of samples

Linear Sampling

Pick samples in chronological order

(Accelerated video footage)

Evaluation

Formative Evaluation (1)

With two domain experts from the field of vibration analysis

Round 1 / 3

Discussion and Requirements

Formative Evaluation (2)

Round 2 / 3

  • Experts used the system
  • Identify a change point
  • Feedback

Formative Evaluation (3)

Round 3 / 3

  • Experts used own dataset.
  • Tried to replicate a finding.
  • Successfully used system to identify leakage in a machine.

Early analysis results:

Final analysis results:

Where does it not work?

Small patterns (e.g. Anomalies) can be hard to detect

Chirp signals are challenging

Conclusion

Progressive approaches provide quick first insights for large data volumes.

Conclusion

Steering the sampling allows experts to incorporate domain knowledge.

Conclusion

Segmenting the signal helps with pattern discovery.

Progressive Visual Segmentation of
Vibration Signals to Detect Change Points

Thank you!

Julian
Rakuschek
Johanna
Schmidt
Udo
Schlegel
Michèle
Posch
Jutta
Isopp
Tobias
Schreck

Open Source

PRESENT and ENVIRON-HYDRO

Slides

Appendix

Future Work (1)

More screen space for small segments

Hao, M., U. Dayal, D. Keim, and T. Schreck (2007). “Multi-Resolution Techniques for Visual Exploration of Large Time-Series Data”.

Future Work (2)

Uncertain boundaries

Instead of hard boundaries

Future Work (3)

Remove excess samples

Hans-Jörg Schulz and Chris Weaver (2024). "Transient Visual Analytics".

Related Work (1)

Ali, Mohammed et al. “TimeCluster: dimension reduction applied to temporal data for visual analytics.”
The Visual Computer 35 (2019): 1013 - 1026.

Related Work (2)

Bernard, J. et al. “TimeSeriesPaths : Projection-Based Explorative Analysis of Multivariate Time Series Data.”
International Conference in Central Europe on Computer Graphics and Visualization (2012).

Related Work (3)

Schmidl, Sebastian et al. “DendroTime: Progressive Hierarchical Clustering for Variable-Length Time Series.” International Conference on Extending Database Technology (2026).