BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop

BearingNAS, a Hardware-Aware Neural Network Architecture Search framework, enables in-sensor intelligent fault diagnosis for bearings using only a laptop CPU, achieving 99.50% diagnostic accuracy on the LSM6DSO16IS Intelligent Sensor Processing Unit (ISPU) from STMicroelectronics. The search converges in under an hour and targets extreme micro-budgets of 4–8 kiB RAM and 16–32 kiB Flash, eliminating the need for expensive GPUs.

arXiv:2607.18287v1 Announce Type: new 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.