Many Condition Monitoring (CM) domains are suffering from the dual challenges of substantial increases in the volumes of data being produced and collected by sensing systems, and the challenges of modelling increasing complexity in the remote monitored systems. These two issues give rise to the problem that fast and reliable data mining of CM data is a computationally demanding task for real-time (or near real-time) applications. We present the use of AURA [1], a class of binary associative network built on correlation matrix memories (CMMs), as an underpinning technology for efficient, scalable pattern recognition in complex and large scale CM applications. AURA is a class of binary neural network. However, it has a number of advantages over standard neural network techniques for CM pattern classification tasks. These include; high levels of data compression, one-pass training for on-line training, a scalable architecture that can be readily mapped onto high performance computing platforms, and a sound theoretical basis to determine the bounds of the system operation. We describe applications illustrating how the AURA system can be optimised to create an extremely efficient and scalable k-nearest neighbour classifier for multi-variate models. We will also illustrate how the one-pass training capability of the AURA system can be used as the basis of normality and exception modelling in complex CM systems. This latter application has particularly powerful advantages for fault detection models in domains which are characterised by highly dynamic trends or drifting in the standard operational mode of a system, and which, as a result, are extremely difficult to accurately model. The application of the AURA techniques will be illustrated with industry led exemplars in the transport and energy sectors.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)