Modified Overcomplete Autoencoder for Anomaly Detection Based on TinyML

Abstract

This letter explores the architecture of tiny machine learning (TinyML). Deploying machine learning into embedded devices is challenging due to the limited computation power and memory space. An experimental setup has been designed for the anomaly detection of a USB fan. We collect the normal data from a USB fan, and abnormal data are simulated using a broken fan blade. Two different speeds, namely, speed 1 and speed 2, have been used to collect the normal data and abnormal data. The normal data collected are used to train the standard autoencoder model and our proposed model modified overcomplete asymmetric autoencoder (MOA), respectively. The trained model is then deployed into a microcontroller, i.e., Arduino Nano 33 BLE Sense. The proposed MOA can achieve 99.23% accuracy, recall of 99.70%, precision of 98.77%, F1 score of 99.23%, and false positive rate of 1.222%. Besides that, our MOA model only occupies 17 kB. Therefore, it can be fitted into most microcontrollers for embedded applications.