Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues

Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics

Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases

A Visual Decision-Support System using Fingerprint Matrices applied to Cyclical Spatio-Temporal Data from Motorsports

Visualizing cyclical spatio-temporal data is an important part of understanding how and why objects move in the context of motorsports, which is critical feedback for drivers to improve their performance. Current methods have problems such as occlusion and loss of context which significantly limit our ability to see and understand vehicle data. Here we demonstrate how the fingerprint matrix method (which is normally used in lexical analysis) can be applied in vehicle motion analysis to overcome these two problems. Compared to traditional methods using traction circle scatterplot displays of acceleration force data from a race car, our prototype design allows decision makers to see individual datapoints in a more concise display. We show that informative but previously-hidden anomalies and patterns become more easily recognized in the data. Our design generalizes to other cyclical spatio-temporal visualization problems involving transportation, medicine, and the natural world

Blue Noise Plots

We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce BlueNoise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study

Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process

Mechanical properties of the most common european woods: A literature review

Wood is an orthotropic material used since ancient time. A literature research about the mechanical properties of density, fracture toughness, modulus of elasticity, and Poisson’s ratio has been done to have a broader view on the subject. The publications relating to the topic were found through the two search engines Scopus and Google Scholar that have yielded several papers, including articles and book sections. In general, there is no standardization on the method of analysis carried out on wood, underlining the great difficulty in studying this complex material. The parameter of density has a great variability and needs a deeper investigation; fracture toughness is not always available in literature, not even in the different directions of the wood sample. Interesting is the modulus of elasticity, which provides a correlation with density, especially in longitudinal section but, again, it needs to be studied in detail. The parameter of Poisson’s ratio is provided as single values in three different directions, but mainly for softwood. All the parameters require a more in-depth study for both softwood and hardwood. Furthermore, the type of analysis, whether experimental or modelling, needs to be standardized to have more comparable results