Implementation of Bayes Theorem-Based Expert System for Heavy Equipment Engine Damage Diagnosis

Abstract

Current failure diagnosis in heavy equipment relies heavily on manual technician analysis, which is time-consuming and subjective. This study develops a Bayesian theorem-based expert system to automate failure detection by quantifying symptom-failure conditional probabilities. Using Agile Scrum methodology, the system integrates a knowledge base of 10 common failure types and 50 associated symptoms from industrial case data. Test results demonstrate the system's ability to identify primary failures (e.g., Hard to Start/K01) with 47.06% accuracy in test cases, reducing diagnosis time from hours to minutes. The implementation shows potential to significantly decrease equipment downtime and maintenance costs in industrial applications.