A multi-condition acoustic dataset of ball bearings for fault diagnosis-compound inner-outer race pitting

This dataset provides multi-condition, long-duration acoustic data for ball bearings. The data were acquired from bearings with normal bearing faults, systematically and separately for each combination of three rotational speeds (800, 1000, and 1200 r/min) and three load levels (0%, 15%, and 30% of the rated torque, where 100% corresponds to 6 N·m).
Acoustic signals were recorded synchronously using three microphones: two B&K Type 4966 and one CRY333. The sensitivities were 49.76 mV/Pa (B&K at Position 2), 46.95 mV/Pa (B&K at Position 3), and 23.98 mV/Pa (CRY333at Position 1), respectively. The detailed microphone placement is described in our associated dataset article to be published in Data in Brief.
For each of the nine operational conditions, a continuous 50-minute recording was captured at a sampling rate of 32,768 Hz. For data management, each 50-minute recording is segmented into five sequential 10-minute files (labeled 1-5) within the repository. The raw data are provided in the proprietary .bkc format. Files are organized and named according to a hierarchical directory structure based on fault type, speed, load, and sampling rate.
This dataset supports analytical tasks such as feature extraction and pattern recognition. It serves as a benchmark for developing and validating fault diagnosis algorithms for rotating machinery, particularly for evaluating model performance under varying operating conditions.

本数据集面向滚动轴承，提供多工况下的长时程声学数据。数据采集自健康轴承与故障轴承，并针对三种转速（800、1000、1200 r/min）与三种负载等级（额定扭矩的0%、15%、30%，其中100%额定扭矩对应6 N·m）的所有组合，开展系统化的独立采集。
声学信号采用三台麦克风同步录制：两台B&K Type 4966型麦克风与一台CRY333型麦克风。三者的灵敏度分别为：位置2处的B&K麦克风49.76 mV/Pa、位置3处的B&K麦克风46.95 mV/Pa，以及位置1处的CRY333麦克风23.98 mV/Pa。麦克风的具体安装位置详见我们即将发表于《Data in Brief》的相关数据集论文。
针对九种工况中的每一种，均以32768 Hz的采样率录制了连续50分钟的声学数据。为便于数据管理，数据集仓库中每份50分钟的录制文件均被分割为5个连续的10分钟子文件（编号1至5）。原始数据采用专属的.bkc格式存储。所有文件按照故障类型、转速、负载及采样率构建的层级目录结构进行组织与命名。
本数据集可支撑特征提取、模式识别等分析任务，同时可作为开发与验证旋转机械故障诊断算法的基准数据集，尤其适用于评估模型在变工况条件下的性能表现。