A Vibration Signal Dataset for Lithology Identification
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901391137570816 |
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| author | Liu, JunSong |
| author_facet | Liu, JunSong |
| contents | <p><strong>Description</strong></p> <p>This dataset is designed for lithology identification and is constructed based on vibration signals collected from a laboratory rock drilling test platform. The experiments were conducted using an ASK-101 drilling machine, and three-axis vibration acceleration signals generated during the drilling process were acquired using a CT1000SLEP vibration sensor.</p> <p>The dataset contains vibration signals from <strong>seven typical lithology classes</strong>. Under <strong>three different drilling conditions</strong>, <strong>15 groups of raw continuous vibration signals</strong> were collected for each lithology class. Each signal has a duration of approximately <strong>20 seconds</strong> and is sampled at <strong>51.2 kHz</strong>. All signals are three-axis measurements, capturing multi-directional dynamic characteristics during the drilling process.</p> <p>The dataset consists of two parts:</p> <ul> <li><strong>rawdata</strong>: raw sampled data stored in Excel format, with a clear structure for ease of understanding and analysis</li> <li><strong>dataset</strong>: a preprocessed subset derived from the raw data, which can be directly used for model training and experiments</li> </ul> <p>In addition, example code is provided to demonstrate basic data usage and processing procedures, enabling users to quickly get started.</p> <p><strong>Dataset Characteristics</strong></p> <ul> <li>Includes multiple lithology classes (7 classes), suitable for classification and identification tasks</li> <li>High sampling rate (51.2 kHz), preserving rich temporal dynamics</li> <li>Collected from real drilling experiments, with strong engineering relevance</li> <li>Suitable for research on measurement-while-drilling (MWD) lithology identification and cross-condition generalization</li> </ul> <p>Under varying drilling parameters (e.g., rotational speed and weight on bit), the distribution of vibration signals exhibits noticeable shifts. Therefore, this dataset can also be used to study <strong>distribution shift</strong> and the generalization capability of models under different operating conditions.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19702433 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Vibration Signal Dataset for Lithology Identification Liu, JunSong <p><strong>Description</strong></p> <p>This dataset is designed for lithology identification and is constructed based on vibration signals collected from a laboratory rock drilling test platform. The experiments were conducted using an ASK-101 drilling machine, and three-axis vibration acceleration signals generated during the drilling process were acquired using a CT1000SLEP vibration sensor.</p> <p>The dataset contains vibration signals from <strong>seven typical lithology classes</strong>. Under <strong>three different drilling conditions</strong>, <strong>15 groups of raw continuous vibration signals</strong> were collected for each lithology class. Each signal has a duration of approximately <strong>20 seconds</strong> and is sampled at <strong>51.2 kHz</strong>. All signals are three-axis measurements, capturing multi-directional dynamic characteristics during the drilling process.</p> <p>The dataset consists of two parts:</p> <ul> <li><strong>rawdata</strong>: raw sampled data stored in Excel format, with a clear structure for ease of understanding and analysis</li> <li><strong>dataset</strong>: a preprocessed subset derived from the raw data, which can be directly used for model training and experiments</li> </ul> <p>In addition, example code is provided to demonstrate basic data usage and processing procedures, enabling users to quickly get started.</p> <p><strong>Dataset Characteristics</strong></p> <ul> <li>Includes multiple lithology classes (7 classes), suitable for classification and identification tasks</li> <li>High sampling rate (51.2 kHz), preserving rich temporal dynamics</li> <li>Collected from real drilling experiments, with strong engineering relevance</li> <li>Suitable for research on measurement-while-drilling (MWD) lithology identification and cross-condition generalization</li> </ul> <p>Under varying drilling parameters (e.g., rotational speed and weight on bit), the distribution of vibration signals exhibits noticeable shifts. Therefore, this dataset can also be used to study <strong>distribution shift</strong> and the generalization capability of models under different operating conditions.</p> <p> </p> |
| title | A Vibration Signal Dataset for Lithology Identification |
| url | https://doi.org/10.5281/zenodo.19702433 |