A Vibration Signal Dataset for Lithology Identification
Year: 2026
Type: dataset
Abstract: Description 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. The dataset contains vibration signals from seven typical lithology classes. Under three different drilling conditions, 15 groups of raw continuous vibration signals were collected for each lithology class. Each signal has a duration of approximately 20 seconds and is sampled at 51.2 kHz. All signals are three-axis measurements, capturing multi-directional dynamic characteristics during the drilling process. The dataset consists of two parts: rawdata: raw sampled data stored in Excel format, with a clear structure for ease of understanding and analysis dataset: a preprocessed subset derived from the raw data, which can be directly used for model training and experiments In addition, example code is provided to demonstrate basic data usage and processing procedures, enabling users to quickly get started. Dataset Characteristics Includes multiple lithology classes (7 classes), suitable for classification and identification tasks High sampling rate (51.2 kHz), preserving rich temporal dynamics Collected from real drilling experiments, with strong engineering relevance Suitable for research on measurement-while-drilling (MWD) lithology identification and cross-condition generalization 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 distribution shift and the generalization capability of models under different operating conditions.
Author: Liu, JunSong
Institution: Chengdu University of Technology
Language: English
Subfield: Fuel Technology
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7155358995