Diagnosis automation using similarity analysis: application to industrial systems

Overview & Synopsis

The paper introduces the MMT-RCA framework, an automated incident diagnosis system crucial for maintaining security and reliability in complex systems such as ABB's Load Position Sensor (LPS) and FAGOR's remote manufacturing machinery access. Traditional incident response methods often involve time-consuming and error-prone manual analysis, hindered by limited human expertise. MMT-RCA addresses this challenge by leveraging similarity analysis techniques. It utilizes historical incident data to create a comprehensive repository, capturing characteristics and outcomes of past incidents. By employing sophisticated algorithms, the MMT-RCA framework identifies patterns and correlations among incidents, facilitating the swift identification of similar problems and their root causes. To validate its efficacy, the framework underwent real-world experiments with industrial data from both companies. The results demonstrate the framework's ability to accurately diagnose incidents and identify root causes.