Instability due to internal damping of rotating shafts

Rotor internal damping has been indicated as one of the main causes of instability in rotating machinery for more than a century. However, the exact characterisation of this damping is still an unsolved research topic. Therefore, in this thesis the consequences of material damping in rotating shafts are examined more in depth. Two main steps are considered. Firstly, a finite element model of the beam, including viscous and hysteretic damping, is constructed. This model allows to calculate the threshold speed of instability and the resonance frequencies of a shaft. Furthermore it allows to vary the damping parameters and to compare the considered models giving an indication of the general relations between instability and damping properties. Secondly, an experimental approach should elucidate which model fits best for the physical damping. In general, the main purpose is to gain new insights into how the damping should really be modelled to have the most accurate and safe prediction of a designed rotor

Combining mechanistic and data-driven techniques for predictive modelling of wastewater treatment plants

Mechanistic models are widely used for modelling of wastewater treatment plants. However, as they are based on simplified and incomplete domain knowledge, they often lack accurate predictive capabilities. In contrast, data-driven models are able to make accurate predictions, but only in the operational regions that are sufficiently described by the dataset used. We investigate an alternative hybrid model, combining mechanistic and data-driven techniques. We show that the hybrid approach combines the strengths of both modelling paradigms. It allows for accurate predictions out of the training dataset without the need for complete domain knowledge. Moreover, this approach is not limited to wastewater treatment plants and can potentially be applied wherever mechanistic models are used

Modal analysis and testing of rotating machines for predictive maintenance: effect of gyroscopic forces

Through the year’s preventive maintenance, which is based on vibration measurements has grown in importance to reduce costs. However in industry nowadays there is still a great need for good analytical prediction models that describe the dynamics of rotating structures. In this paper it is first shown how to build a model for for undamped gyroscopic systems. Secondly the model is analysed and used to make a parametric study for the design a test rig. It is found that the third and the fourth eigenfrequency changein function of the rotation speed due to the gyroscopic effect. Finally the experimental modal testing of rotating structures is discussed. The methods to excite a rotating structure in order to obtain the modal parameters are studied

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p

Análisis de Pseudomonas fitopatógenas usando métodos inteligentes de aprendizaje un enfoque general sobre taxonomía y análisis de ácidos grasos dentro del género Pseudomonas

The identification of plant-pathogenic bacteria is often of high importance. In this paper, we evaluate the identification of plant-pathogenic species within the genus Pseudomonas by fatty acid methyl ester (FAME) analysis. Starting from a FAME database, high quality data sets were generated. Two research questions were investigated: can plant-pathogenic Pseudomonas species be discriminated from each other and can the group of plant-pathogenic Pseudomonas species be distinguished from the group of non-plant-pathogenic Pseudomonas species. In a first stage, a principal component analysis was performed to evaluate the variability within the data. Secondly, the machine learning method Random Forests was evaluated for identification purposes. This intelligent method allows to learn from the variability and patterns in the data and to improve the species identification. The principal component analysis of plant-pathogenic species clearly showed overlapping data clouds. A Random Forests model was developed that achieved a species identification performance of 71.1%. Discriminating the group of plant-pathogenic species from the group of non-plant- pathogenic species was more straightforward, given by the Random Forests identification performance of 85.9%. Moreover, it was shown that a statistical relation exists between the fatty acid profiles and plant pathogenesis

Stator current measurements as a condition monitoring technology &#x2014; The-state-of-the-art

Condition monitoring of electrical machines has proven to be economically beneficial within industrial production sites. This paper illustrates the technical implications of implementing Motor Current Signature Analysis (MCSA) as a tool for condition monitoring. The majority of machine failures are illustrated and are related to the state-of-the-art of MCSA. Because MCSA has become a valuable tool within the broader scope of condition monitoring during the last decade, a vast amount of new research opportunities can be presented. One of these opportunities is to determine Frequency Response Functions (FRFs) between the rotor vibrations and the stator current as a function of the operating point of the machine. This allows to estimate the mechanical machine fault vibrations out of the stator current frequency components, independently of its speed and load. This paper ends by presenting a research strategy to obtain this goal