An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Abstract

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edgeaware applications of equipment such as automobiles and ships.

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves

“…Approaches in the literature that are implemented on microcontrollers rely on CNNs, RNNs, LSTM and their variations [ 24 ]. These applications are related, for example, to the estimation of Ion-Li battery parameters [ 5 ], structural health monitoring [ 28 ], rail vehicle running states [ 29 ], ECG Monitoring [ 30 ] or the eye blink detection [ 31 ]. All of those approaches use at least one ARM Cortex-M4 that includes FPU and specific DSP instructions.…”