Fault Detection System Using Machine Learning in IoT Based Automobile Industries

Billions of physical devices around the world connected to the internet refer to the latest booming technology such as internet of things (IoT). Without human intervention, all collecting and sharing data around the globe. An automobile is one of the familiar mechanized industries around the world. For the past four years, introduce various pioneering features in vehicles that result is enormously high car sales in the automobile sector. Internet of Things (IoT) is one of the most recent technologies that plateful automobile industry to make next-generation of vehicles. Using the IoT based automobiles techniques are more renovate the way that people can feel easily to interact with the vehicles. Sensors play a crucial role in IoT systems for predicting various types of information within vehicles. If a sensor malfunctions, it can jeopardize the entire system. Therefore, detecting sensor faults is essential in modern IoT technology. In the proposed work, a fault detection system is implemented using machine learning within IoT-integrated automobiles. The Random Forest Classifier (RFC) algorithm is utilized to predict and identify sensor faults in vehicles. The performance metrics shows the random forest classifier produces best performance when compared to the other existing Logistic Regression for sensor fault detection and other conventional algorithm.