MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido,, Kaori Suefusa, and Yohei Kawaguchi

TL;DR
The paper introduces the MIMII dataset, a comprehensive collection of normal and anomalous industrial machine sounds recorded in real factory environments, aimed at advancing machine fault detection and maintenance automation.
Contribution
It provides the first publicly available dataset of industrial machine sounds under real operating conditions, including various fault types, to support research in acoustic anomaly detection.
Findings
Dataset includes sounds from valves, pumps, fans, and slide rails.
Contains recordings of contamination, leakage, unbalance, and rail damage.
Aims to facilitate development of automated maintenance systems.
Abstract
Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and classification of scenes and events, but there are no public datasets that focus on the sound of industrial machines under normal and anomalous operating conditions in real factory environments. In this paper, we present a new dataset of industrial machine sounds that we call a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). Normal sounds were recorded for different types of industrial machines (i.e., valves, pumps, fans, and slide rails), and to resemble a real-life scenario, various anomalous sounds were recorded (e.g.,…
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Taxonomy
TopicsMusic and Audio Processing · Speech and Audio Processing · Anomaly Detection Techniques and Applications
