1,720,957 research outputs found
Classifier ensembles to improve the robustness to noise of bearing fault diagnosis
No full textIn this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40. 55 to -11. 35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e. g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest
Time Evolution analysis of bearing faults
No full textThis paper proposes a study and a method for automatic detection and diagnosis of defects of rolling element bearings. We use classification techniques (QDC and neural networks) and classifier fusion. We exploit experimental data consisting of vibration signals represented in the frequency domain by means of the Fast Fourier Transform, registered by two accelerometers. We consider one defect, namely indentation on the roll, with three different severity levels, with the data related to the lowest severity level collected in four subsequent days. We achieve high classification accuracy in all the experiments, which aim, respectively, to identify the defects as soon as they appear, to identify the defects as time passes, to train the classifier on defects collected in the first day and test it on signals collected in the following days, and, finally, to analyze how a specific defect evolves over time. In particular, by analyzing how the vibration signals of a damaged bearing evolve over time, we observe that, as time passes, the signals representing the least severe damage get more similar to those related to the same defect but with a higher severity level. This study can be profitably used to define when bearing maintenance should be performed
Noise assessment in the diagnosis of rolling element bearings
No full textIn this paper we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of an accelerometer and we consider six levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 db to 9.59 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise, then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we manage to significantly increase the classification accuracy
Short-time forecasting of renewable production energy in solar photovoltaic installations
No full textIn this paper we describe an automatic system that is able to perform real-time monitoring of a photovoltaic system and short-time forecasting of the production energy. By comparing the theoretical production energy with the real production energy one we can easily detect losses in efficiency. The proposed system was tested on data collected from a photovoltaic installation with two fixed arrays (each connected to an inverter) of solar panels. We made use of two types of least squares regression, the linear regression (LR) and the quadratic regression (QR). The best results were obtained by the QR algorithm using one week as training set for each inverter
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Rolling element bearing diagnosis using convex hull
No full textIn this paper, we compare traditional classifiers, such as Linear and Quadratic Discriminant Classifiers and neural networks, with a one-class classifier, namely, convex hull. With reference to rolling element bearing diagnosis, we show that convex hull outperforms traditional classifiers in the classification of faults and different levels of fault severity not known during the training phase
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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