Advanced education and training in bioenergy in Europe

The paper explains how bioenergy education and training is growing in Europe. Employment estimates are included for renewable energy in general, and bioenergy in particular, to highlight the need for a broadly based education and training programme that is essential to build a knowledgeable workforce that can drive Europe's growing bioenergy sector. The paper reviews current provisions in bioenergy at Masters and PhD levels across the 27 members of the EU (EU27) plus Norway and Switzerland. This identifies a very active and expanding bioenergy education provision. 65 English-language Masters Courses in bioenergy (either focussing completely on bioenergy or with significant bioenergy content or specialisation) were identified. 231 providers of PhD studies in bioenergy were found.Masters Course offerings have grown rapidly across Europe during the last five years, but where data is available, enrolment has been quite low suggesting that there is an oversupply of courses and that course organisers are being optimistic in their projections. Existing provisions in Europe at Masters and PhD levels are clearly more than sufficient for short term needs, but further work is needed to evaluate the take-up rate and the content and focus of the provisions. To ensure talented graduates are attracted to these programmes, better promotion, stronger links with the research community and industry, and increased collaboration among course providers are needed. Short Courses of two to five days are an excellent way of meeting post-experience training needs but require further growth and development to serve the needs of the bioenergy community. © 2011 Elsevier Ltd.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.