Treffer: Assessment of Real-Time Monitoring of Catalytic Converters Performance Using Advanced Sensor Technology in Motor Vehicles

The degradation of catalytic converter (CC) efficiency over time poses a critical challenge to vehicular emission control and environmental sustainability. Traditional diagnostic methods, such as periodic inspections, often fail to detect real-time performance deterioration, resulting in delayed maintenance and excessive emissions. This study addresses this problem by developing a real-time monitoring system for CC performance using advanced sensor technologies and machine learning. The justification lies in the urgent need for accurate, continuous diagnostics to meet stringent emission regulations and improve vehicle efficiency. The methodology integrates oxygen (O₂), temperature, and NOₓ sensors with statistical techniques time-series analysis, regression modeling, and Principal Component Analysis (PCA) to detect trends and anomalies. Machine learning models, including Support Vector Machines (SVM) and Random Forests (RF), are applied to classify CC health status and predict degradation patterns. Data processing and analysis are performed using MATLAB, Python, R, and LabVIEW. Results show a significant improvement in fault detection accuracy and predictive maintenance efficiency, enhancing emission control and vehicle performance. The system enables early detection of catalyst inefficiency, reducing environmental impact and operational costs. Recommendations for further study include enhancing sensor calibration accuracy, refining machine learning model generalization, and improving real-time analytics to support broader implementation. Automotive manufacturers are urged to adopt these intelligent diagnostic frameworks within on-board diagnostic systems to advance sustainable, real-time vehicle emission management.