Motorized Vehicle Diagnosis Design Using the Internet of Things Concept with the Help of Tsukamoto's Fuzzy Logic Algorithm

Abstract

There are many popular branches, including the Internet of Things (IoT) and Artificial Intelligence (AI), which have solved many problems. Same as that, the automotive field is also growing with the technology of OBD-II. Unfortunately, not many people are familiar with OBD-II even though the features offered are very varied to prevent vehicle damage. This proposed work uses an IoT and AI system to make a vehicle diagnosis system with a help of OBD-II technology. By using ESP32 to collect data in each vehicle and using one Mini-PC to run the diagnosis with Fuzzy Logic Tsukamoto for three or more vehicles, this work can decrease the research cost. This work also uses the Fuzzy Logic Tsukamoto to diagnose vehicle health which is considered very suitable in real-time data situations. The method that we proposed is using Iterative Waterfall because of its simplicity and because there is a feedback path in every step. Iterative Waterfall is divided into 4 stages, Requirement Gathering and Analysis, System Design, implementation of Development, and Testing. Numerical validation is included by using MAPE for the testing in the IoT system and AI system. According to the MAPE result for the IoT system, the engine off voltage is 0.9510789847% and the engine start voltage is 3.136217503% which is considered a very good result. The MAPE result for the AI system is quite high, which is 20.74364412%, and because of that, the AI system needed more research for better performance. Overall, the system that has been proposed is already successful in monitoring vehicle health based on the parameters that have been determined.

AI-Driven Intelligent Transportation Systems in the Age of 5G/6G Networks

“…The LSTM model is a kind of recurrent neural network (RNN) created to recognise long-term relationships in sequential data, such time series or text [4]. It overcomes the vanishing gradient problem that traditional RNNs face by introducing specialized memory cells that selectively retain and update information over long sequences.Figure1 represents the basic structure of the LSTM.…”