MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2]. This dataset is made available by Hitachi, Ltd. under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. A baseline sample code for anomaly detection is available on GitHub: https://github.com/MIMII-hitachi/mimii_baseline/ *1: This version "public 1.0" contains four models (model ID 00, 02, 04, and 06). The rest three models will be released in a future edition. [1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019. [2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.

本数据集为面向工业机器故障排查与检测的音频数据集（MIMII Dataset）。其包含四类工业机械产生的音频，即阀门、泵、风扇与滑轨。每类机械均包含7款独立产品型号，每个型号的数据均涵盖正常工况音频（时长5000秒至10000秒）与异常工况音频（时长约1000秒）。为贴合真实工业场景，本次录制涵盖多种异常工况音频，例如污染、泄漏、旋转失衡以及滑轨损坏。同时，研究团队将多间真实工厂录制的背景噪声与机械音频进行混叠。所有音频均由八通道麦克风阵列录制，采样率为16 kHz，每样本位深度为16 bit。MIMII数据集可作为基于音频的机器故障诊断研究的基准测试集，用户可针对特定任务测试模型性能，例如无监督异常检测、迁移学习、噪声鲁棒性等。数据集详细信息参见文献[1][2]。本数据集由日立有限公司（Hitachi, Ltd.）基于知识共享署名-相同方式共享4.0国际（CC BY-SA 4.0）许可协议发布。异常检测的基准示例代码可在GitHub获取：https://github.com/MIMII-hitachi/mimii_baseline/ *1：本次公开的1.0版本仅包含4款型号（型号ID为00、02、04与06），剩余3款型号将在后续版本中发布。[1] Harsh Purohit、Ryo Tanabe、Kenji Ichige、Takashi Endo、Yuki Nikaido、Kaori Suefusa与Yohei Kawaguchi，"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection"，arXiv预印本arXiv:1909.09347，2019年。[2] Harsh Purohit、Ryo Tanabe、Kenji Ichige、Takashi Endo、Yuki Nikaido、Kaori Suefusa与Yohei Kawaguchi，"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection"，收录于第4届声学场景检测与分类研讨会（DCASE）论文集，2019年。

搜集汇总

背景与挑战

背景概述

MIMII数据集是一个专注于工业机器故障调查的声音数据集，包含四种工业机器（阀门、泵、风扇、滑轨）的正常和异常声音记录，适用于机器故障诊断和无监督异常检测等任务。数据集采用八通道麦克风阵列采集，采样率为16kHz，旨在模拟真实工业场景并提供基准测试支持。