This paper deals with the fault classification of centrifugal pumps, based on the residuum between the output and the input of an Autoencoding Convolutional Neural Network previously trained for abnormal behaviour detection. The proposed classification method performs a dimensional reduction of the residuum vector using Principal Component Analysis, and then, based on the first 3 principal components, classifies the data, using a simple rule-based algorithm, in one of the classes: normal, clogged filter, broken fan blade, detached rotor section and other fault source. The classification method proved to be reliable in an industrial application, providing a 90% correct identification of the machine condition.

This paper presents the generalised token based networking (gTBN) architecture, which enables dynamic binding of communities and their applications to specialised network services. gTBN uses protocol independent tokens to provide decoupling of authorisation from time of usage as well as identification of network traffic. The tokenised traffic allows specialised software components uploaded into network elements to execute services specific to communities. A reference implementation of gTBN over IPv4 is proposed as well as the presentation of our experiments. These experiments include validation tests of our test bed with common grid applications such as GridFTP, OpenMPI, and VLC. In addition, we present a firewalling use case based on gTBN.