A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms

In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimisation problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of WF fault diagnosis

A holistic integrated dynamic design and modelling approach applied to the development of ultraprecision micro-milling machines

Ultraprecision machines with small footprints or micro-machines are highly desirable for micro-manufacturing high-precision micro-mechanical components. However, the development of the machines is still at the nascent stage by working on an individual machine basis and hence lacks generic scientific approach and design guidelines. Using computer models to predict the dynamic performance of ultraprecision machine tools can help manufacturers substantially reduce the lead time and cost of developing new machines. Furthermore, the machine dynamic performance depends not only upon the mechanical structure and components but also the control system and electronic drives. This paper proposed a holistic integrated dynamic design and modelling approach, which supports analysis and optimization of the overall machine dynamic performance at the early design stage. Based on the proposed approach the modelling and simulation process on a novel 5-axis bench-top ultraprecision micro-milling machine tool – UltraMill – is presented. The modelling and simulation cover the dynamics of the machine structure, moving components, control system and the machining process, and are used to predict the overall machine performance of two typical configurations. Preliminary machining trials have been carried out and provided the evidence of the approach being helpful to assure the machine performing right at the first setup

On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces