Abstract
The deployment of autonomous predictive vibration-based diagnostics in Edge-IIoT requires balancing the cost of Micro-Electro-Mechanical System (MEMS) accelerometers against their metrological limitations. This study proposes a parametric Sensor Digital Twin (SDT) framework for virtual benchmarking of measurement chains at the hardware-software co-design stage. The SDT model emulates mechanical and electrical filtering, aliasing, noise, and quantization; its fidelity was validated experimentally using a physical ADXL345 sensor, with an RMS noise error of 8.56%. Virtual profiles of the commercial ADXL345 and ADXL357 sensors were generated using the reference NLN-EMP centrifugal pump dataset acquired with a Wilcoxon 786B-10 piezoelectric accelerometer. Their diagnostic performance was evaluated using eight diagnostic features and five heterogeneous machine-learning algorithms under interpolation, forward extrapolation, and backward extrapolation scenarios across fault severity levels. In the interpolation scenario, the ADXL345 and ADXL357 profiles achieved Macro F1-scores of 0.8999 and 0.8947, respectively, compared with 0.9567 for the reference measurement chain. In the forward extrapolation scenario, the ADXL357 profile achieved a Macro F1-score of 0.7171, compared with 0.6768 for the reference measurement chain. The results confirm that the SDT can support sensor hardware selection through virtual benchmarking, while the considered MEMS accelerometers provide comparable diagnostic performance in the evaluated scenarios when a representative training dataset is available.