Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound

Oh, Dong Yul; Yun, Il Dong

doi:10.3390/s18051308

Cited by 104 publications

(76 citation statements)

References 14 publications

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“…In [16] Kawaguchi and Endo used an end-to-end Long Short-Term Memory (LSTM) autoencoder on subsampled signal using audio data for anomaly detection. In [17] Oh and Yun used autoencoder on sound data from a Surface-Mounted Device machine. And specifically, to the best of our knowledge, only Purohit et al in [13], used an unsupervised learning-based method on the MIMII dataset.…”

Section: Several Approaches Such As Neural Network (Nn)mentioning

confidence: 99%

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WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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One of the main focuses of smart industry is machinery failure predictive solutions. To achieve this, IoT-based solutions have been widely deployed. However, data processing and decision making remain challenging. The absence of enough knowledge has been the primarily limitation of statistical methods and supervised learning methods. Therefore, unsupervised learning methods are gaining more popularity but still have limits to cover effectively the pre-signs of failures due to the complexity of training process and results visualization. Previously, we proposed a novel Big Data Analysis method on audio/vibration data to cover effectively the pre-signs of failures through data visualization without complex learning or processing. We validated our proposal on a demo system. In the present work, we are using part of the MIMII dataset to test our proposed analysis method on a real-world-like data and verify the validity of our proposal on a more complex system. We are showing that we can detect abnormal machine behaviors and predict failures without prior training or knowledge of the target monitored machine.

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Section: Several Approaches Such As Neural Network (Nn)mentioning

confidence: 99%

“…Machines have been known to be monitored via the acquisition of certain sensor data: voltage and current [2], temperature and pressure [3], vibration [4,5,6,7,8,9,10] and sound [11,12,13,14,15,16,17]. Vibration and sound have been reported effective sensor signals to characterize a machine behavior.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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“…Our main goal is to detect anomalous sounds in an unsupervised learning scenario, as discussed in Section 1. Several studies have successfully used autoencoders for unsupervised anomaly detection [12,[22][23][24], so here, we evaluate an autoencoder-based unsupervised anomaly detector. We used only the first channel of microphones ("No.…”

Section: Methodsmentioning

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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“…Oh et al [26] propose an AD strategy based on an auto-encoder to detect audio anomalies produced by a Surface-Mounted Device (SMD) machine that places components on top of a Printed Circuit Board (PCB). The algorithm creates an auto-encoding manifold able to measure differences among instances and the manifold, signaling an anomaly if such distances are too large.…”

Section: Related Workmentioning

confidence: 99%

IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge

Janjua

Vecchio

Antonini

et al. 2019

Engineering Applications of Artificial Intelligence

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In the recent years, a rapid growth of IoT devices has been observed, which in turn results in a huge amount of data produced from multiple sources towards the most disparate cloud platforms or the Internet in general. In a typical cloud-centric approach, the data produced by these devices is simply transmitted over the Internet, for consumption and/or storage. However, with the exponential growth in data production rates, the available network resources are becoming the actual bottleneck of this huge data flowing. Therefore, several challenges are appearing in the coming years, which are mainly related to data transmission, processing, and storage along the so-called cloud-to-thing continuum. In fact, one of the most critical requirements of several IoT applications is low latency, which often hinders raw data consumption to happen at the opposite endpoint with respect to its production. In the context of IoT data stream analytics, for instance, the detection of anomalies or rare-events is one of the most demanding tasks, as it needs prompt detection to increase its significance. In this respect, Fog and Edge Computing seem to be the correct paradigms to alleviate these stringent demands in terms of latency and bandwidth as, by leveraging on re-configurable IoT gateways and smart devices able to support the distribution of the overall computational task, they envisage to liquefy data processing along the way from the sensing device to a cloud endpoint. In this paper, we will present IRESE, that is a rare-event detection system able to apply unsupervised machine learning techniques on the incoming data, directly on affordable gateways located in the IoT edge. Notwithstanding the proposed approach enjoys the benefits of a fully unsupervised learning approach, such as the ability to learn from unlabeled data, it has been tested against various audio rare-event categories, such as gunshot, glass break, scream, and siren, achieving precision and recall measures above 90% in detecting such events.

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Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound

Cited by 104 publications

References 14 publications

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

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

IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge

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