TinyML for Fault Detection in Rotating Machinery: A Post-Training Quantization Assessment on the MAFAULDA Dataset - Carvalho de Lucena Filho, Cândido Alfredo; Valencia de Almeida, Felipe (Anais do Brazilian e-Science Workshop (BreSci), 2026)
Unplanned downtime in rotating machinery is a major cost driver in industrial operations, and early fault detection through vibration analysis can prevent it. Running these classifiers directly on microcontrollers (TinyML) reduces inference latency and enables faster on-device response, but it is still unclear how different model architectures and quantization schemes behave under strict memory and flash constraints. This paper evaluates the feasibility of embedding rotating-machinery fault machine learning classifiers on low-cost microcontrollers. We compare MLP, 1D-CNN, LSTM and Random Forest on the MAFAULDA dataset, using 132 features per window and stratified group k-fold cross-validation to prevent leakage across windows from the same file. Three post-training quantization schemes (FP32, FP16, INT8) are evaluated against the requirements of the ESP32-S3, STM32F4, and ESP32-WROOM. The INT8 MLP was the most efficient model conforming to all three platforms (37 KB, F1=0.98), reducing size by 67% over its FP32 counterpart with negligible accuracy loss. The 1D-CNN lost accuracy under INT8 (F1 from 0.94 to 0.75), and the unrolled LSTM grew 3× rather than shrinking, results that highlight the need to jointly evaluate the model and quantization, not in isolation.