five

A multi-condition acoustic dataset of ball bearings for fault diagnosis-normal bearing

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Mendeley Data2026-04-18 收录
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https://data.mendeley.com/datasets/j99rf8rsrz
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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.

本数据集涵盖滚珠轴承在多工况下的长时程声学数据。数据采集自正常轴承与故障轴承,针对三种转速(800、1000、1200 r/min)与三种负载等级(额定转矩的0%、15%、30%,其中100%额定转矩对应6 N·m)的全部组合,开展了系统化独立采集。 采用三台麦克风同步采集声学信号,分别为两台B&K Type 4966型麦克风与一台CRY333型麦克风。各麦克风的灵敏度依次为: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、位置1处的CRY333麦克风23.98 mV/Pa。麦克风的具体布置方式将在将于《Data in Brief》发表的相关数据集论文中详细说明。 针对九种运行工况中的每一种,均以32768 Hz的采样率采集了连续50分钟的声学记录。为便于数据管理,存储库中每份50分钟的记录被分割为5个连续的10分钟文件(编号1至5)。原始数据采用专属.bkc格式存储。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,可作为旋转机械故障诊断算法开发与验证的基准数据集,尤其适用于评估模型在变工况条件下的性能表现。
创建时间:
2026-02-10
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