Modelling of Asymmetric Rotor and Cracked Shaft

Ishibashi, Tatsuro; Yoshida, Akitoshi; Kawai, Tadao

doi:10.3384/ecp18148180

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…In the past decade, industrial Internet of Things (IoT) and datadriven techniques have been revolutionizing the manufacturing industry, and different approaches have been undertaken for monitoring the state of machinery. Examples include vibration sensorbased approaches [1][2][3][4], temperature sensor-based approaches [5], and pressure sensor-based approaches [6]. Another approach is to detect anomalies from sound by using technologies for acoustic scene classification and event detection [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

Purohit¹,

Tanabe²,

Ichige³

et al. 2019

Proceedings of the Detection and Classification of Acoustic Scenes And Events 2019 Workshop (DCASE2019)

223

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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., contamination, leakage, rotating unbalance, and rail damage). The purpose of releasing the MIMII dataset is to assist the machine-learning and signalprocessing community with their development of automated facility maintenance.

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