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Mechanical Anomaly Detection on an Embedded Microcontroller
Abstract
This paper explores machine learning on an embedded device to detect anomalies with sophisticated low-power neural networks. We leverage this deep learning approach to detect mechanical anomalies as they occur on a top-load washing machine. We collect normal data from balanced laundry loads and abnormal data from unbalanced laundry loads, as they are being washed by the machine. The normal data is then used to train two different neural network models: autoencoder and variational autoencoder. This model is ported to an Arduino Nano microcontroller mounted to the washing machine. Using the autoencoder model, the microcontroller detects unbalanced washing machine loads with 92% accuracy, 90% precision and 99% recall. The battery life for this autoencoder model is 20 hours on 5 V lithium batteries, which is only 14.9% less than the life of a basic LED-blink application on the same platform.
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Authors in [135] developed a TinyML model deployed in STMicroelectronic STM32H743Z12 to detect anomalies in rotating machinery.
“The authors in [135] developed TinyML model deployed in STMicroelectronic STM32H743Z12 to detect anomalies in rotating machinery.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [135]
Citations
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References 14 · Cited by 13
Research works printed but not linked: 3
- abadi, 0, TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow org
- lord, 2021, TinyML Anomaly Detection, Master’s thesis
- pete warden, 2019, TinyML
Metadata
- Authors
- Lord, M., & Kaplan, A.
- Affiliations
- California State University, Northridge (US)
- Venue
- 2021 International Conference on Computational Science and Computational Intelligence (CSCI)
- Publisher
- IEEE
- Published
- 2021-12
- Pages
- 562-568
- OpenAlex
- W4285328131
- Cited by
- 13
- References
- 14
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 3 · 2022: 1
- FWCI
- 3.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computational Science and Computational Intelligence (CSCI) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- anomaly detection, mechanical fault detection, embedded machine learning, low-power neural networks, autoencoder, variational autoencoder, Arduino Nano, washing machines, battery life, edge computing
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