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

Dong Yul Oh1, Il Dong Yun2

  • 1Department of Digital Information Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea. dyoh@hufs.ac.kr.

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|April 27, 2018
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Summary
This summary is machine-generated.

This study introduces an auto-encoder model to detect abnormal machine sounds and outliers. The novel approach reduces annotation costs by using reconstruction error to identify anomalies in complex machinery.

Keywords:
SMDanomaly detectionauto-encodermachine soundunsupervised learning

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Area of Science:

  • Machine learning
  • Signal processing
  • Industrial monitoring

Background:

  • Detecting anomalies in machine operations is crucial for maintenance and safety.
  • Deep learning approaches are increasingly used for anomaly detection.
  • Manual annotation of abnormal data is time-consuming and costly.

Purpose of the Study:

  • To propose a novel method for detecting abnormal operation sounds and outliers in complex machinery.
  • To reduce the data-driven annotation cost associated with anomaly detection.
  • To develop an auto-encoder based model for identifying anomalies.

Main Methods:

  • Utilized an auto-encoder architecture for anomaly detection.
  • Employed residual error, representing reconstruction quality, as the anomaly indicator.
  • Assessed the model using complex Surface-Mounted Device (SMD) machine sound data.

Main Results:

  • Achieved state-of-the-art performance in anomaly detection.
  • Successfully identified abnormal sounds and outliers in complex machine data.
  • Demonstrated the model's effectiveness in reducing annotation costs.

Conclusions:

  • The proposed auto-encoder model effectively detects anomalies in complex machine sounds.
  • The method offers a cost-effective solution for anomaly detection by minimizing manual annotation.
  • This approach advances the field of machine monitoring and predictive maintenance.