Hose-Vibration Dataset: Triaxial ESP32–ADXL345 Recordings for Six-Class Operational State Classification

This dataset contains triaxial vibration measurements collected from two flexible water hose of 1 inch (wide) and 0.5 inch (narrow) diameter under five operational states. The signals were recorded using an ESP32–ADXL345 sensor module mounted with a rigid 3D-printed clamp to ensure consistent mechanical coupling. The objective of this dataset is to support research on lightweight vibration-based diagnostics, condition monitoring, and small-footprint classification models. The dataset is provided as a single combined CSV file consisting of normalized and label-corrected time-series windows, ready for machine learning workflows. Contents The dataset includes: Three-axis acceleration signals (columns: x, y, z) Window-level class labels derived by majority voting over raw samples Balanced representation across all classes Class Definitions (6 Classes) Class ID Description 0 Normal flow (no fault) 1 Twisted 2 Blocked 3 Loose Join 4 Minor leak 5 Large leak Data Collection Setup Sensor: ADXL345 accelerometer Controller: ESP32 microcontroller Mount: Custom rigid clamp to eliminate placement variability Environment: Indoor laboratory with steady water flow (≈1.5–2.0 bar) Pipes Used: Wide-radius and narrow-radius hose geometries Use Cases This dataset is suitable for: Lightweight deep learning models (e.g., 1D CNNs) Classical ML baselines (e.g., Random Forest, SVM) Domain adaptation / cross-condition generalization Few-shot learning for IoT sensing Vibration-based fault detection research本数据集包含针对两种直径分别为1英寸（宽口径）与0.5英寸（窄口径）的柔性水管采集的三轴振动测量数据，涵盖五种运行工况。信号由搭载刚性3D打印夹具的ESP32–ADXL345传感器模块录制，以确保稳定的机械耦合。本数据集旨在为基于轻量化振动的故障诊断、状态监测以及轻量型分类模型的相关研究提供支撑。 本数据集以单个合并CSV文件形式提供，包含经归一化与标签校正后的时序窗口数据，可直接用于机器学习工作流。 数据集内容如下： - 三轴加速度信号（字段：x、y、z） - 基于原始样本多数投票得到的窗口级类别标签 - 各类别样本分布均衡 类别定义（共6类） | 类别ID | 类别描述 | | ---- | ---- | | 0 | 正常流量（无故障） | | 1 | 管路扭结 | | 2 | 管路堵塞 | | 3 | 接头松动 | | 4 | 轻微泄漏 | | 5 | 严重泄漏 | 数据采集设置 - 传感器：ADXL345加速度计 - 控制器：ESP32微控制器 - 安装夹具：定制刚性夹具，以消除安装位置偏差 - 实验环境：室内实验室，水流压力稳定在≈1.5–2.0巴 - 所用管路：宽口径与窄口径柔性水管 适用场景 本数据集适用于： - 轻量级深度学习模型（如一维卷积神经网络（1D CNNs）） - 经典机器学习基准测试（如随机森林、支持向量机（SVM）） - 域自适应/跨工况泛化研究 - 物联网传感相关的少样本学习 - 基于振动的故障检测研究