Cited by 17Neural Networks and Reservoir ComputingAnomaly Detection Techniques and ApplicationsAdvanced Neural Network Applications

Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

Open access
Cardoni, Matteo, Pau, Danilo Pietro, Falaschetti, Laura, Turchetti, Claudio, Lattuada, Marco·2021·Electronics

Abstract

The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.

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Crossref · 10.3390/electronics10222836

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  • Authors in [138] created a TinyML model deployed on ESP-WROOM-32 MCU device to detect anomalies in thermal images; it sends data only when it detects an anomaly on a machine to the server using MQTT.

    “The authors in [138] created a TinyML model deployed on ESP-WROOM-32 MCU device to detect anomalies in thermal images; it sends data only when it detects an anomaly on a machine to the server using Message Queuing Telemetry Transport(MQTT) protocol.”

    From A Comprehensive Survey on TinyML (2023) · cites this paper as [138]

    The Abstract describes an MCU-based model for detecting oil-leak anomalies in images. However, Section 8 names an STM32H743ZI2 MCU, and Section 4.1 describes RGB images, not thermal images. The cited passages do not support the claim that data are sent only upon detecting an anomaly, or that MQTT is used. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • The results in [138] show around 94% accuracy and 92% f1-score.

    “The results show around 94% accuracy and 92% f1-score.”

    From A Comprehensive Survey on TinyML (2023) · cites this paper as [138]

    Section 7 describes the accuracy tests in terms of false-positive and false-negative rates, and the Conclusions report those rates under different noise conditions. Searches for “94% accuracy,” “92% f1-score,” and “F1-score” found no passage reporting the claimed figures. The reported rates alone do not establish those two metrics. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 2

  1. Basseville, M.; Nikiforov, I.V. Detection of Abrupt Changes: Theory and Application; Prentice Hall Englewood Cliffs: Hoboken, NJ, USA, 1993; Volume 104.
  2. Fadnavis, S. Image Interpolation Techniques in Digital Image Processing: An Overview. Int. J. Eng. Res. Appl. 2014, 4, 2248-2270.

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Authors
Cardoni, M., Pau, D. P., Falaschetti, L., Turchetti, C., & Lattuada, M.
Affiliations
  • Marche Polytechnic University (IT)
  • STMicroelectronics (Italy) (IT)
Corresponding author
Pau, Danilo Pietro
Venue
Electronics
Publisher
MDPI AG
Published
2021-11-18
Volume
10
Issue
22
Pages
2836
ISSN
2079-9292 (ISSN-L)
OpenAlex
W3214107103
Cited by
17
References
76
Citations by year
2026: 5 · 2025: 1 · 2024: 2 · 2023: 6 · 2022: 3
FWCI
0.97
Type
journal article
Language
en
Open access
Locations
  • Electronics (publishedVersion, cc-by)
  • Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
oil leak detection, wind turbines, anomaly detection, reservoir computing, 1-bit quantization, binarization, extreme learning machine, microcontroller deployment, low-power machine learning, multi-camera surveillance

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