ALADDIN - enhancing applicability and scalability

The ALADDIN project aims at the study and development of flexible, accurate, and reliable techniques and principles for computerised event classification and fault diagnosis for complex machinery and industrial processes. The main focus of the project is on advanced numerical techniques, such as wavelets, and empirical modelling with neural networks. This document reports on recent important advancements, which significantly widen the practical applicability of the developed principles, both in terms of flexibility of use, and in terms of scalability to large problem domains. In particular, two novel techniques are here described. The first, which we call Wavelet On- Line Pre-processing (WOLP), is aimed at extracting, on-line, relevant dynamic features from the process data streams. This technique allows a system a greater flexibility in detecting and processing transients at a range of different time scales. The second technique, which we call Autonomous Recursive Task Decomposition (ARTD), is aimed at tackling the problem of constructing a classifier able to discriminate among a large number of different event/fault classes, which is often the case when the application domain is a complex industrial process. ARTD also allows for incremental application development (i.e. the incremental addition of new classes to an existing classifier, without the need of retraining the entire system), and for simplified application maintenance. The description of these novel techniques is complemented by reports of quantitative experiments that show in practice the extent of these improvements. (Author)