Airbus Helicopter Accelerometer Dataset 核心 · 已核验
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The use case is relative to flight test helicopters vibration measurements. The dataset has been collected and released by Airbus SAS. A main challenge in flight tests of heavily instrumented aircraft (helicopters or airplanes alike) is the validation of the generated data because of the number of signals to validate. Manual validation requires too much time and manpower. Automation of this validation is crucial.
In this case, different accelerometers are placed at different positions of the helicopter, in different directions (longitudinal, vertical, lateral) to measure the vibration levels in all operating conditions of the helicopter. The data set consists of multiple 1D time series with a constant frequency of 1024 Hz taken from different flights, cut into 1 minute sequences.
We are interested in the detection of abnormal sensor behaviour. Sensors are recorded at 1024Hz and we provide sequences of one-minute length.
Training data
The training dataset is composed of 1677 one-minute-sequences @1024Hz of accelerometer data measured on test helicopters at various locations, in various angles (X, Y, Z), on different flights. All data has been multiplied by a factor so that absolute values are meaningless, but no other normalization procedure was carried out. All sequences are considered as normal and should be used to learn normal behaviour of accelerometer data.
Validation Data
The validation dataset is composed of 594 one-minute-sequences of accelerometer data measured on test helicopters at various locations, in various angles (X, Y, Z). Locations and angles may or may not be identical to those of the training dataset. Sequences are to be tested with the normal behaviour learnt from the training data to detect abnormal behaviour. The amount of abnormal sequences in the validation dataset is a priori unknown.
Datasets are provided in a HDF5 format that can be decoded by many standard machine learning modules (like pandas for instance):
• In th
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- 落地页
- https://www.research-collection.ethz.ch/handle/20.500.11850/415151
- 许可证
- CC-BY-NC-SA-4.0 (置信:verified_official)
- 国内可访问性
-
国内直连:未知 (2026-07-11 检测)
非直连:未知 (2026-07-11 检测)
「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。 - 发布年份
- 2020
- 发布方
- ETH Zurich
- 设备类型
helicopter- PHM 任务
anomaly_detection
传感器
| sensor_type: accelerometerobserved_property: vibration_acceleration |
运行工况
| description: 试飞序列工况(工业匿名化)condition_type: otheris_varying: True |
关联论文(8 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- Airbus Helicopter Accelerometer Dataset 2020 · 首发该数据集
- From Manned to Unmanned Helicopters: A Transformer-Driven Cross-Scale Transfer Learning Framework for Vibration-Based Anomaly Detection 2026 · 用于迁移/跨工况
- Multimodal Machine Learning in Prognostics and Health Management of Manufacturing Systems 2023 · 候选引用(未核验)
- Invertible Neural Network for Time Series Anomaly Detection 2023 · 候选引用(未核验)
- Invertible Neural Network for Inference Pipeline Anomaly Detection 2023 · 候选引用(未核验)
- Edge-Compatible Convolutional Autoencoder Implemented on FPGA for Anomaly Detection in Vibration Condition-Based Monitoring 2022 · 用于方法验证
- Computational Reproducibility Within Prognostics and Health Management 2022 · 候选引用(未核验)
- Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms 2021 · 用于方法验证
溯源(9 条)
| 日期: 2026-07-08 |
| 来源链接: https://api.datacite.org/dois/10.3929/ethz-b-000415151 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-10 |
| 日期: 2026-07-11 |
| 日期: 2026-07-14 |
| 日期: 2026-07-25 |
| 日期: 2026-07-31 |