A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines

Rodrigo de Paula Monteiro, Mariela Cerrada Lozada, Diego Roman Cabrera Mendieta, René Vinicio Sánchez Loja, Carmelo José Albanez Bastos Filho

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Anomaly detection in time series is an important task to many applications, e.g, the maintenance policies of rotating machines within industries strongly rely on time series monitoring. Rotating machines are vital elements within industries. Therefore, maintenance policies on these critical elements concern the quality of products and safety issues. Condition-based maintenance is an example of those policies. In this context, we propose a novel method to train a deep learning-based feature extractor for the anomaly detection problem on rotating machinery. It consists of using a prototype selection algorithm to improve the training process of a randomly initialized feature extractor. We perform this process iteratively using data belonging to one probability distribution, i.e., the normal class. We carried the prototype selection out with the Nearest Neighbors algorithm, and the feature extractor was a Convolutional Neural Network. We validate the method on three datasets of spectrograms related to gearbox and compressors faults and achieved promising results. We obtained detection rates in anomalous data close to 100%, and the anomaly detectors classified normal instances with accuracy values superior to 95%. Those results were competitive concerning other deep learning-based anomaly detectors in the literature, with the advantage of being an integrated solution.

Original languageEnglish
Article number117528
JournalExpert Systems with Applications
StatePublished - 15 Oct 2022

Bibliographical note

Funding Information:
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001 , and Universidad Politecnica Salesiana through the research group GIDTEC.

Publisher Copyright:
© 2022 Elsevier Ltd


  • Anomaly detection
  • Deep learning
  • Prototype selection
  • Rotating machinery


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