Acoustic Anomaly Detection Using Convolutional Autoencoders in Industrial Processes

Taha Berkay Duman*, Barış Bayram, Gökhan İnce

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

Abstract

In the industrial plants, detection of abnormal events during the processes is a difficult task for human operators who need to monitor the production. In this work, the main aim is to detect anomalies in the industrial processes by an intelligent audio based solution for the new generation of factories. Therefore, this paper presents a Convolutional Autoencoder (CAE) based end-to-end unsupervised Acoustic Anomaly Detection (AAD) system to be used in the context of industrial plants and processes. In this research, a new industrial acoustic dataset has been created by gathering the audio data obtained from a number of videos of industrial processes, recorded in factories involving industrial tools and processes. Due to the fact that the anomalous events in real life are rather rare and the creation of these events is highly costly, anomaly event sounds are superimposed to regular factory soundscape by using different Signal-to-Noise Ratio (SNR) values. To show the effectiveness of the proposed system, the performances of the feature extraction and the AAD are evaluated. The comparison has been made between CAE, One-Class Support Vector Machine (OCSVM), and a hybrid approach of them (CAE-OCSVM) under various SNRs for different anomaly and process sounds. The results showed that CAE with the end-to-end strategy outperforms OCSVM while the respective results are close to the results of hybrid approach.

Original languageEnglish
Title of host publication14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings
EditorsJosé António Sáez Muñoz, Emilio Corchado, Héctor Quintián, Francisco Martínez Álvarez, Alicia Troncoso Lora
PublisherSpringer Verlag
Pages432-442
Number of pages11
ISBN (Print)9783030200541
DOIs
Publication statusPublished - 2020
Event14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019 - Seville, Spain
Duration: 13 May 201915 May 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume950
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019
Country/TerritorySpain
CitySeville
Period13/05/1915/05/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Anomaly detection
  • Audio feature extraction
  • Convolutional autoencoders
  • Industrial processes
  • One-Class Support Vector Machine
  • Signal-to-Noise Ratio

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