Diesel engine damage prediction using IoT-based temperature sensors: Integrated hybrid neural network-fuzzy

Diesel engines are widely used in various sectors such as industry. Damage to diesel engines can cause production disruptions and significant financial losses. Diesel engine damage can be caused by excessively high engine temperatures (overheating) and is characterized by high exhaust emissions. This study aims to develop an expert system to predict diesel engine damage integrated with IoT-based temperature sensors with machine learning algorithms. The machine learning model developed in this study uses a hybrid artificial neural network and fuzzy logic. This study uses a research and development method, which is divided into 3 stages. The first stage is the creation of a temperature sensor-based data logger system. The second stage is the simulation of a hybrid Neural network-fuzzy prediction model for diesel engine damage. The third stage is the integration of the machine learning model into the expert system for predicting diesel engine damage. The results of the study show that the system can work to store and display diesel engine temperatures along with engine conditions, namely normal, medium, and high. The results of the simulation of diesel engine damage prediction with the ANFIS model obtained RMSE = 0.2065 and R² = 0.9273, which indicates a high level of accuracy and generalization ability.