Acoustic waveforms generated by several types of partial discharges in laboratory environment 核心 · 已核验

doi:10.5281/zenodo.20049046

Overview

This dataset contains acoustic waveforms (effective bandwidth 25–100 kHz, ultrasonic range) generated by three types of partial discharges, in air, in a void, and corona discharge at a needle tip, together with a reference recording of ambient noise. Signals were captured in a controlled environment (anechoic chamber) using laboratory-grade measurement equipment.

The dataset is intended for training and benchmarking machine learning models, particularly artificial neural networks, for the classification of partial discharge (PD) types from airborne ultrasonic acoustic emissions.

Contents at a glance:

4000 waveforms total (1000 per class × 4 classes)

Each waveform: 125 samples, 250 µs duration, 500 kS/s sampling rate

Values stored in pascals (already calibrated from microphone voltage)

Effective measurement bandwidth: 25–100 kHz

File format: CSV (one waveform per row, one sample per column)

Discharge sources and specimens

Partial discharges were generated using three distinct configurations:

Air discharge — discharges between two conductive parts separated by an air gap of 2.5 mm.

Void discharge — internal partial discharges inside a 3D-printed insulator model (SLA) with an internal air void of cylindrical shape with diameter of 3 mm and 1 mm thickness.

Corona discharge — generated at the tip of a needle electrode (50 um tip radius, 2.5 mm to the grounded counter-electrode).

Voltage was applied using a GLASSMAN FJ Series FJ50R2.4 high-voltage source (120 W, up to 50 kV). The applied voltage for each class is encoded in the file name (e.g. PD_in_Air_12kV.csv):

Measurement setup

Acoustic waveforms were acquired using the following signal chain:

Microphone: Brüel & Kjær 4939, 1/4″ free-field condenser microphone (usable range ~4 Hz to 100 kHz, ±2 dB up to ~80 kHz)

Microphone preamplifier: Brüel & Kjær 2670; overall sensitivity (microphone+preamplifier) 4.6 mV/Pa

Signal preamplifier: GRAS 12AQ at 20 dB gain, with the 20 kHz analog high-pass filter enabled

Data acquisition: National Instruments PXI-5922 flexible-resolution digitizer, configured to 24-bit resolution, input range ±200 mV, sampling at 500 kS/s

Acquisition trigger: Rogowski coil placed on the incoming high-voltage wire to the specimen — each waveform is triggered by the current pulse associated with a discharge event on the HV side of the test setup

Post-acquisition filtering: 6th-order Bessel band-pass filter, 25–100 kHz (digital)

The microphone was positioned 1 m from the discharge source in all measurements. Recordings were performed inside an anechoic chamber under stable laboratory conditions (22 °C, approx. 40 % RH).

The waveforms stored in the dataset are the band-pass-filtered output; raw unfiltered signals are not included. The effective measurement bandwidth of 25–100 kHz is set by the GRAS 12AQ analog high-pass filter (20 kHz) combined with the digital Bessel band-pass (25–100 kHz). The 500 kS/s sampling rate provides a Nyquist frequency of 250 kHz, well above the upper passband edge.

Triggering on the Rogowski-coil current signal ensures that each captured 250 µs window is centered on (or includes) an actual discharge event rather than random ambient noise. The reference noise file was recorded with the high-voltage source connected but no discharge activity present, so it captures the ambient acoustic noise of the chamber together with the noise floor of the full acquisition chain.

Files

The dataset comprises four CSV files. Each file contains 1000 rows (one waveform per row) and 125 columns (one column per time sample). Values are acoustic pressure in pascals.

File

Class

Voltage

Shape

PD_in_Air_12kV.csv

Air discharge

12 kV

1000 × 125

PD_in_Void_25kV.csv

Void discharge

25 kV

1000 × 125

Corona_11kV.csv

Corona discharge

11 kV

1000 × 125

Noise_NoPD.csv

No discharge (ambient + acquisition chain)

0 kV

1000 × 125

Loading the data (Python)

Minimal example — load one file and inspect its contents:

import numpy as np

import pandas as pd

FS = 500_000 # sampling rate, 500 kS/s

N_SAMPLES = 125

DURATION_US = N_SAMPLES / FS * 1e6 # 250 µs

# Each row is one waveform; values are acoustic pressure in pascals.

waveforms = pd.read_csv("PD_in_Air_12kV.csv", header=None).values

print(waveforms.shape) # (1000, 125)

# Time axis in microseconds

t_us = np.arange(N_SAMPLES) / FS * 1e6

Loading all four classes into ML-ready arrays:

import numpy as np

import pandas as pd

files = {

0: "PD_in_Air_12kV.csv",

1: "PD_in_Void_25kV.csv",

2: "Corona_11kV.csv",

3: "Noise_NoPD.csv",

}

X_parts, y_parts = [], []

for label, path in files.items():

data = pd.read_csv(path, header=None).values # (1000, 125)

X_parts.append(data)

y_parts.append(np.full(len(data), label, dtype=np.int64))

X = np.concatenate(X_parts, axis=0) # (4000, 125), pascals

y = np.concatenate(y_parts, axis=0) # (4000,), class labels

Suggested applications

Classification of partial discharge type from acoustic signals (ANN, CNN, RNN, classical ML)

Benchmarking of acoustic feature extractors (spectrograms, MFCCs, wavelets) on ultrasonic PD data

Development of acoustic-based PD detection and diagnostics for high-voltage insulation systems

Acoustic emission analysis in the 25–100 kHz ultrasonic band

Limitations and notes

The dataset reflects measurements taken at a fixed microphone-to-source distance of 1 m inside an anechoic chamber; performance of models trained on this data may differ in reverberant or noisier industrial environments.

All waveforms have been band-pass filtered (25–100 kHz); information outside this band is not recoverable from the stored signals.

Each class corresponds to a single applied voltage level. Voltage-dependence of the discharge signature is therefore not captured.

Triggering is performed on the HV-side current signal via the Rogowski coil; the time alignment of the acoustic waveform within the 250 µs window therefore reflects the acoustic time-of-flight from the source to the microphone (~3 ms at 1 m in air at 22 °C), with the Rogowski-coil trigger providing the time reference.

Acknowledgements

This dataset was created as a part of the Listen2Future project. Under grant 101096884, Listen2Future is co-funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the Chips Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them. The project is supported by the CHIPS JU and its members (including top-up funding by Austria, Belgium, Czech Republic, Germany, Netherlands, Norway, and Spain).

The collection of the dataset was also supported by the infrastructure of RICAIP, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 857306, and from the Ministry of Education, Youth and Sports under OP RDE grant agreement No. CZ.02.1.01/0.0/0.0/17_043/0010085.

落地页

https://zenodo.org/doi/10.5281/zenodo.20049046

许可证

CC-BY-4.0 (判读置信:inferred)

国内可访问性

国内直连:直连超时(慢或不稳定,非封锁证据) (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

PHM 任务

fault_diagnosis fault_detection

故障工况

传感器