Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

Bayram B., Duman T. B., İnce G.

EXPERT SYSTEMS, cilt.38, sa.1, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1111/exsy.12564
  • Dergi Adı: EXPERT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Psycinfo, Library, Information Science & Technology Abstracts (LISTA)
  • Anahtar Kelimeler: acoustic anomaly detection, audio feature extraction, convolutional autoencoder, convolutional long short-term memory autoencoder, industrial processes
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking.