Although modelling techniques for railway vehicle dynamics are well-developed, much of the development in railway vehicle condition monitoring has relied on the knowledge-based techniques where no analytical system model is employed and only qualitative or empirical system knowledge are used for fault diagnosis. In this paper, the model-based approach is proposed as an alternative for railway v...

Apart from regular wheel wear such as decreasing wheel diameters and reduced flange thicknesses and heights, wheel flats (WF) and oval wheels (or OOR, Out-Of-Roundness) are the most common wheelset problems for railway vehicles. Within the FP7 Research Project “Saferail”, APT has developed an innovative wheelset monitoring system so-called “Wheel flat and Out of Roundness Monitoring (WORM) syst...

In this paper, visualization and neural network techniques are applied together to a power transformer condition monitoring system. Through visualizing the data from the chromatogram of oil-dissolved gases by 2-D and/or 3-D graphs, the potential failures of the power transformers become easy to be identified. Through employing some specific neural network techniques, the data from the chromatog...

rangeland health assessment provides qualitative information on ecosystemattributes. we examined changes in rangeland health in the zagros semi-arid rangelands ofiran at fixed sites between 2001 and 2011, over the 10-year period. the rangelandfragments significantly declines in the quality of the vegetation, and changes in plantspecies were driven largely by seasonality, and to a lesser extent,...

The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically r...

Novelty Detection is an approach to classification that offers advantages over conventional techniques for monitoring high-integrity systems and manufacturing processes: it is not necessary to provide fault data to the system during development. Instead, providing a sufficiently comprehensive model of the system‟s normal behaviour has been formed, deviations from normal working conditions can b...

We introduce an approach for predicting the behaviour of a machine during a production cycle. Typical data analysis methods assume that continuous behaviour is (fully) observed. This assumption is unrealistic as monitored machines are often interrupted and restarted at irregular points in time. We study the resulting problem, propose a solution and report on a use-case in wire drawing.

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been ...

We propose a novel algorithm for extracting samples from a data set supporting the extremal points in the set. Since the density of the data set is not taken into account, the method could enable adaptation to gradually changing data characteristics (e.g. machine wear). Using knowledge about the clustering structure of the data (obtained with multidimensional scaling techniques), the complexity...