PMMA plate with different AE sources

This dataset consists of AE signals generated by different sources on a PMMA plate. It is intended for studying and experimenting with data-based approaches for AE source classification.

The PMMA plate is excited using three distinct AE sources:

1. Pencil-lead breaks (Hsu-Nielsen source with 0.5 mm leads)
2. Pulsing by the AMSY-6 acoustic emission measurement system with 30 Vpp
3. Salt trickling from a salt shaker, positioned approximately 20 cm away

YouTube Video

Structure

• Material: Polymethyl methacrylate (PMMA)
• Dimensions: 300 x 300 x 9.7 mm

Measurement chain

• Vallen AMSY-6 AE measurement system with ASIP-2A signal processor cards
• 4x Vallen VS150-RSC sensors with 34 dB preamplifier gain
• Measurement settings:
  • Filter: 95-300 kHz
  • Threshold: 40 dBAE
  • Duration discrimination time: 1000 µs
  • TR (transient/raw data):
    • Samplerate: 2500 kHz
    • Pretrigger samples: 100
    • Post-duration samples: 1000

Sensor positions

The sensors are arranged in a square configuration on the PMMA plate:

Channel	X [mm]	Y [mm]
1	50	50
2	250	50
3	50	250
4	250	250

Files

All files can be downloaded here: https://filetransfer.vallen.de/openae/datasets/pmma-plate-sources/.

Each measurement consists of two SQLite3 database files:

• *.pridb: The primary database, that stores just the AE features (amplitude, energy, counts, duration, rise time, ...) and the index of the transient/raw data TRAI. Use the table view_ae_data.
• *.tradb: The transient database, that stores the raw data for each hit record referenced by the TRAI index. Use the table view_tr_data. The raw data is stored in the Data column in binary format (int16 values). The factor from column TR_mV can be used to transform the integer values to millivolts.

You may want to use the Python library vallenae to easily read the measurement files.

Link	Source	Hits
plbs.pridb plbs.tradb	Pencil-lead breaks	266
pulse.pridb pulse.tradb	Pulsing	292
salt.pridb salt.tradb	Salt trickling	282

Examples

• classifier-openae.ipynb: SVM classifier trained with OpenAE features
• classifier-standard.ipynb: SVM classifier trained with standard AE features