BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
BearingNAS is a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift intelligence onto sensor dies via in-sensor processing. It targets extreme micro-budgets (4-8 KiB RAM, 16-32 KiB Flash) and uses a lightweight, derivative-free search strategy that runs on a laptop CPU in under an hour. Evaluated on the CWRU bearing benchmark, the best architecture achieves 99.50% accuracy on the STMicroelectronics ISPU, demonstrating viability of low-cost, production-scale bearing fault diagnosis.
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[Submitted on 30 Jun 2026]
Title:BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
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Abstract:This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework on the Case Western Reserve University (CWRU) bearing benchmark, optimizing architectures for three STMicroelectronics targets: two commodity microcontrollers and the LSM6DSO16IS Intelligent Sensor Processing Unit (ISPU). Running entirely on a laptop CPU, the search converges in less than an hour. The resulting best in-sensor architecture achieves a highly competitive diagnostic accuracy of 99.50\% on the ISPU. These results demonstrate the viability of shifting the machine learning workload inside the sensor package, enabling low-cost, production-scale bearing fault diagnosis.
Comments: Accepted to IEEE COINS 2026 in Special Session 7: Edge AI technologies
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.18287 [cs.LG]
(or arXiv:2607.18287v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.18287
arXiv-issued DOI via DataCite
Submission history
From: Andrea Mattia Garavagno [view email] [v1] Tue, 30 Jun 2026 11:28:46 UTC (95 KB)
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