Optimisation of correlation matrix memory prognostic and diagnostic systems

Condition monitoring systems for prognostics and diagnostics can enable large and complex systems to be operated more safely, at a lower cost and have a longer lifetime than is possible without them. AURA Alert is a condition monitoring system that uses a fast approximate k Nearest Neighbour (kNN) search of a timeseries database containing known system states to identify anomalous system behaviour. This search algorithm, AURA kNN, uses a type of binary associative neural network called a Correlation Matrix Memory (CMM) to facilitate the search of the historical database. AURA kNN is evaluated with respect to the state of the art Locality Sensitive Hashing (LSH) approximate kNN algorithm and shown to be orders of magnitude slower to search large historical databases. As a result, it is determined that the standard AURA kNN scales poorly for large historical databases. A novel method for generating CMM input tokens called Weighted Overlap Code Construction is presented and combined with Baum Coded output tokens to reduce the query time of the CMM. These modi cations are shown to improve the ability of AURA kNN to scale with large databases, but this comes at the cost of accuracy. In the best case an AURA kNN search is 3.1 times faster than LSH with an accuracy penalty of 4% on databases with 1000 features and fewer than 100,000 samples. However the modi ed AURA kNN is still slower than LSH with databases with fewer features or more samples. These results suggest that it may be possible for AURA kNN to be improved so that it is competitive with the state of the art LSH algorithm.