A Graph-Based Approach to Automatic Car Reliability System Prediction with Probabilistic Modeling and Machine Learning

Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system reliability prognosis. By breaking complex dependency among vehicles’ components into a hierarchy and using past failure data, the method improves on prediction error. Real-world automotive datasets show excellent results with better resolution and higher accuracy for the enhanced components compared to existing techniques while at the same time providing interpretability by visualizing the dependency of the components.