Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.

The increased availability of modern embedded many-core architectures supporting floating-point operations in hardware makes them interesting targets in traditional high performance computing areas as well. In this paper, the Lattice Boltzmann Method (LBM) from the domain of Computational Fluid Dynamics (CFD) is evaluated on Adapteva’s Epiphany many-core architecture. Although the LBM implementation shows very good scalability and high floating-point efficiency in the lattice computations, current Epiphany hardware does not provide adequate amounts of either local memory or external memory bandwidth to provide a good foundation for simulation of the large problems commonly encountered in real CFD applications.