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Cited by 83Machine Fault Diagnosis TechniquesFault Detection and Control SystemsAnomaly Detection Techniques and Applications

Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data

Open access
Serradilla, Oscar, Zugasti, Ekhi, Ramirez de Okariz, Julian, Rodriguez, Jon, Zurutuza, Urko·2021·Applied Sciences
DOI 10.3390/app11167376
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Abstract

Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong to correct working conditions. Thereby, semi-supervised data-driven models are relevant to enable PdM application by learning from assets’ data. However, their main challenges for application in industry are achieving high accuracy on anomaly detection, diagnosis of novel failures, and adaptability to changing environmental and operational conditions (EOC). This article aims to tackle these challenges, experimenting with algorithms in press machine data of a production line. Initially, state-of-the-art and classic data-driven anomaly detection model performance is compared, including 2D autoencoder, null-space, principal component analysis (PCA), one-class support vector machines (OC-SVM), and extreme learning machine (ELM) algorithms. Then, diagnosis tools are developed supported on autoencoder’s latent space feature vector, including clustering and projection algorithms to cluster data of synthetic failure types semi-supervised. In addition, explainable artificial intelligence techniques have enabled to track the autoencoder’s loss with input data to detect anomalous signals. Finally, transfer learning is applied to adapt autoencoders to changing EOC data of the same process. The data-driven techniques used in this work can be adapted to address other industrial use cases, helping stakeholders gain trust and thus promote the adoption of data-driven PdM systems in smart factories.

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  • The effort required to investigate an anomaly is usually proportional to the number of features that describe it.

    “Siddiqui et al. [193] point out that the effort required to investigate an anomaly is usually proportional to the number of features that describe it.”

    From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [193]

    Section 3.2 (Diagnosis; page not provided) discusses reducing feature-vector dimensionality and using explanations to identify anomalous signals. It does not say that investigation effort is usually proportional to the number of features describing an anomaly. Searches for that relationship and for investigation effort found no supporting passage in our copy. (LLM, conf=0.72)

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Cites

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Research works printed but not linked: 6

  1. Wang, K., Wang, Y., Strandhagen, J.O., Yu, T., Eds.; Springer: Singapore, 2018; pp. 1-9.
  2. Colemen, C.; Damodaran, S.; Chandramoulin, M.; Deuel, E. Making Maintenance Smarter; Deloitte University Press: New York, NY, USA, 2017.
  3. Serradilla, O.; Zugasti, E.; Zurutuza, U. Deep learning models for predictive maintenance: A survey, comparison, challenges and prospect. arXiv 2020, arXiv:2010.03207. · arXiv
  4. Talmoudi, S.; Kanada, T.; Hirata, Y. Tracking and visualizing signs of degradation for an early failure prediction of a rolling bearing. arXiv 2020, arXiv:2011.09086. · arXiv
  5. Wen, T.; Keyes, R. Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning. arXiv 2019, arXiv:1905.13628. · arXiv
  6. Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; Wattenberg, M. Smoothgrad: Removing noise by adding noise. arXiv 2017, arXiv:1706.03825. · arXiv

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Related

Authors
Serradilla, O., Zugasti, E., Ramirez de Okariz, J., Rodriguez, J., & Zurutuza, U.
Affiliations
  • Mondragon Unibertsitatea (ES)
  • Koniker, San Andres Auzoa 20, 20500 Mondragon, Spain
Corresponding author
Serradilla, Oscar
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2021-08-11
Volume
11
Issue
16
Pages
7376
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app11167376
OpenAlex
W3208111191
Cited by
83
References
31
Citations by year
2026: 12 · 2025: 25 · 2024: 21 · 2023: 15 · 2022: 8 · 2021: 2
FWCI
5.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • eRepository Mondragon University (Mondragon University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Provincial Council of Gipuzkoa (FA/OF326/2020(ES))
  • European Union’s Horizon 2020 research and innovation programme (825030)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Keywords
predictive maintenance, anomaly detection, fault diagnosis, press machines, semi-supervised learning, autoencoder, explainable AI, transfer learning, latent space clustering, one-class support vector machine, extreme learning machine, principal component analysis, smart factory

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