Data mining and visualization of diagnostic messages for condition monitoring

Complex technical systems consist of a huge amount of electromechanical subsystems and components with more or less interdependencies. During the operational phase these systems are subject to wear and tear and other degradation mechanisms. Therefore condition monitoring is a major challenge for operators of such systems. A well-established practice of implementing condition monitoring is to equip the system, or its functionally relevant components with appropriate sensors and measurement technology. Selection of sensors and diagnostic algorithms requires a deep understanding of physical correlations which is usually a core competence of the system provider. Moreover, subsequent installation of a condition monitoring system using additional sensors in most cases is attended by an interference with the system and may affect its functionality if constructional changes of the system for sensor mounting become necessary. A more simple way to access data for technical diagnosis is the use of an on board diagnosis system that collects the system messages that rise while the system is in operation. To control and monitor functionality and interaction between subsystems, each of them generates internal state variables and communicates a subset of these as system messages via task specific bus systems. Here the challenge is to find a suitable way of analyzing the messages. The present paper discusses how to handle system messages for condition monitoring purposes by using data mining and visualization