Real Time Data Processing Techniques

Various Signal Processing Techniques are available for analysis of real time data relative to reference data:

 

Data Cluster method – This involves recording the characteristics of a parameter of a subsystem under different simulated conditions and then using this as a reference to validate the real time data. This method is different from template matching, since it not entirely based on matching the plotted characteristics.

 

Template matching – Entails comparing complete data sets with pre-recorded examples of data resulting from known fault conditions. The method can be used effectively in some circumstances, provided a representation of the data that produces good discrimination between pattern classes can be made. However, this requires a substantial amount of experimentation with different transformations of the data sets to find such distinctions, and would be a computationally intensive process.

 

Statistical and decision theoretic methods – Matches are made based on statistical features of the signal. For example, the mean and peak-to-peak value are evaluated for each vector, and plotted in feature space, whereby different patterns are distinguishable because they form clusters for each class that are located apart from the fully functioning case.

 

Structural or syntactic methods – Involves deconstructing a pattern or vector into structural components, to enable comparisons to be made on more simple, sub-segments of data rather than a complete vector. Mathematically, these methods are similar to fractal-based compression routines.