Abstract
The paper presents an introductory approach to multidimensional condition monitoring of mechanical systems in operation, in particular machines. This generalisation to multidimensionality is possible by utilising the transformed symptom observation matrix, and by successive application of singular value decomposition (SVD). On this basis, one can make it possible to obtain full extraction of fault-related information from symptom observation matrix by traditional monitoring technology. Moreover, by SVD we can create several independent fault measures and indices, and also some combined measures of system overall condition. In other words, full utilisation of SVD enables us to pass from multidimensional-non-orthogonal symptom space , to orthogonal generalised fault space, of much reduced dimension. This seems to be important, as it can increase reliability of condition monitoring of critical systems in operation. It enables also to maximise the amount of condition-related information in the primary symptom observation matrix, and redesign the traditional CM system.
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