A Bayesian based Intelligent Troubleshooting System

Abstract

Troubleshooting systems can bring different benefits in assets management, particularly for service operations, facilitating the diagnosis of problems and faulty components identification. However, these systems are commonly based on rigid computation logic unable to handle uncertainties. In this work, a knowledge-based system exploiting the Bayesian theorem was developed and applied in a troubleshooting tool that relies on human-machine interaction. The required knowledge and the algorithm were analyzed and tested to ensure robustness and self-learning capabilities. Subsequently, the system was implemented in an industrial environment, specifically from a crane manufacturing company. The algorithm is robust to errors and provides the possibility of not answering some questions. However, the system performance is highly dependent on the questions, both in terms of quantity (adequate number compared to possible failures) and quality (effective to discriminate among failures). Indeed, this work shows how the system knowledge enhancement by introducing additional questions can significantly improve the troubleshooting performance. Future developments may involve user-friendliness enhancement and self-learning implementation to add and update questions over time.