Fluid Power Applications Using Self-Organising Maps in Condition Monitoring

Condition monitoring of systems and detection of changes in the systems are of significant importance for an automated system, whether it is for production, transport, amusement, or any other application. Although condition monitoring is already widely used in machinery, the need for it is growing, especially as systems become increasingly autonomous and self-contained. One of the toughest tasks concerning embedded condition monitoring is to extract the useful information and conclusions from the often large amount of measured data. The use of self-organising maps, soms, for embedded condition monitoring is of interest for the component manufacturer who lacks information about how the component is to be used by the system integrator, or in what applications and load cases. At the same time, there is also a potential interest on the part of the system builders. Although they know how the system is designed and will be used, it is still hard to identify all possible failure modes. A component does not break at all locations or in all functions simultaneously, but rather in one, more stressed, location. Where is this location? Here, the collection of as much data as possible from the system and then processing it with the aid of soms allows the system integrators to create a map of the load on the system in its operating conditions. This gives the system integrators a better chance to decide where to improve the system. Automating monitoring and analysis means not only being able to collect prodigious amounts of measured data, but also being able to interpret the data and transform it into useful information, e.g. conclusions about the state of the system. However, as will be argued in this thesis, drawing the conclusions is one thing, being able to interpret the conclusions is another, not least concerning the credibility of the conclusions drawn. This has proven to be particularly true for simple mechanical systems like pneumatics in the manufacturing industry.