Datasets › DATA-PHM
DATA-PHM
🔍 About data: Our database is designed to be a key tool in the advancement of practical research and application in rotating machinery monitoring. It covers a variety of operating conditions and fault types, making the data applicable to a wide range of industrial scenarios.
🛠 Python Package: This database is provided with a Python package, developed by a P.hD Khaled Benaggoune, which automates the downloading of data, the loading of metadata, and preparing the training and testing splits. This tool is designed to save researchers valuable time, enabling them to concentrate on developing and deploying conditional monitoring algorithms.
💡 Objective: You are invited to review the paper via the following DOI link https://doi.org/10.36001/ijphm.2023.v14i2.3497 to find out more about the data content and the remote access links. Also, the associated codes for the preparation of each database, to be used in your own research and development projects, are available on the following Github: https://github.com/khaledbenag/machinery.
📝 Below the DOI links of the papers that used these data : https://doi.org/10.1016/j.measurement.2019.03.065 ; https://doi.org/10.1016/j.ymssp.2020.106680 ; https://doi.org/10.1007/s10845-022-01919-y
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
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License archive 2025-07-28
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Modalities archive 2025-07-28
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Languages archive 2025-07-28
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Variants archive 2025-07-28
- DATA-PHM
1 variant name, as the archive lists them.
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