100,310 research outputs found
Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series
No full textStatistical parametric methodologies are widely employed in the analysis of time series of gas turbine (GT) sensor readings. These methodologies identify outliers as a consequence of excessive deviation from a statistical-based model, derived from available observations. Among parametric techniques, the k–σ methodology demonstrates its effectiveness in the analysis of stationary time series. Furthermore, the simplicity and the clarity of this approach justify its direct application to industry. On the other hand, the k–σ methodology usually proves to be unable to adapt to dynamic time series since it identifies observations in a transient as outliers. As this limitation is caused by the nature of the methodology itself, two improved approaches are considered in this paper in addition to the standard k–σ methodology. The two proposed methodologies maintain the same rejection rule of the standard k–σ methodology, but differ in the portions of the time series from which statistical parameters (mean and standard deviation) are inferred. The first approach performs statistical inference by considering all observations prior to the current one, which are assumed reliable, plus a forward window containing a specified number of future observations. The second approach proposed in this paper is based on a moving window scheme. Simulated data are used to tune the parameters of the proposed improved methodologies and to prove their effectiveness in adapting to dynamic time series. The moving window approach is found to be the best on simulated data in terms of true positive rate (TPR), false negative rate (FNR), and false positive rate (FPR). Therefore, the performance of the moving window approach is further assessed toward both different simulated scenarios and field data taken on a GT.</jats:p
A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)
No full textAnomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation.
To this purpose, a comprehensive and straightforward approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e. the Anomaly Detection Algorithm (ADA) and the Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, inter-sensor statistical analysis (sensor voting) and single-sensor statistical analysis. Anomalies in the time series are identified by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes.
The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens gas turbine in operation. The results show that the DICDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.</jats:p
Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation
No full textThe reliability of gas turbine (GT) health state monitoring and forecasting depends on the quality of sensor measurements directly taken from the unit. Outlier detection techniques have acquired a major importance, as they are capable of removing anomalous measurements and improve data quality. To this purpose, statistical parametric methodologies are widely employed thanks to the limited knowledge of the specific unit required to perform the analysis. The backward and forward moving window (BFMW) k–σ methodology proved its effectiveness in a previous study performed by the authors, to also manage dynamic time series, i.e., during a transient. However, the estimators used by the k–σ methodology are usually characterized by low statistical robustness and resistance. This paper aims at evaluating the benefits of implementing robust statistical estimators for the BFMW framework. Three different approaches are considered in this paper. The first methodology, k-MAD, replaces mean and standard deviation (SD) of the k–σ methodology with median and mean absolute deviation (MAD), respectively. The second methodology, σ-MAD, is a novel hybrid scheme combining the k–σ and the k-MAD methodologies for the backward and the forward windows, respectively. Finally, the biweight methodology implements biweight mean and biweight SD as location and dispersion estimators. First, the parameters of these methodologies are tuned and the respective performance is compared by means of simulated data. Different scenarios are considered to evaluate statistical efficiency, robustness, and resistance. Subsequently, the performance of these methodologies is further investigated by injecting outliers in field datasets taken on selected Siemens GTs. Results prove that all the investigated methodologies are suitable for outlier identification. Advantages and drawbacks of each methodology allow the identification of different scenarios in which their application can be most effective.</jats:p
Letter, [Author unclear] to Paulina T. Merritt
No full textHandwritten letter to Paulina Merritt from an unknown author, October 1, 1876.
Handwritten biographical information on Paulina T. McClung Merritt
No full textA handwritten biography of Paulina T. McClung Merritt by an unknown author, 1892.
Heterogeneous and tissue-specific regulation of effector T cell responses by IFN-gamma during Plasmodium berghei ANKA infection.
No full textIFN-γ and T cells are both required for the development of experimental cerebral malaria during Plasmodium berghei ANKA infection. Surprisingly, however, the role of IFN-γ in shaping the effector CD4(+) and CD8(+) T cell response during this infection has not been examined in detail. To address this, we have compared the effector T cell responses in wild-type and IFN-γ(-/-) mice during P. berghei ANKA infection. The expansion of splenic CD4(+) and CD8(+) T cells during P. berghei ANKA infection was unaffected by the absence of IFN-γ, but the contraction phase of the T cell response was significantly attenuated. Splenic T cell activation and effector function were essentially normal in IFN-γ(-/-) mice; however, the migration to, and accumulation of, effector CD4(+) and CD8(+) T cells in the lung, liver, and brain was altered in IFN-γ(-/-) mice. Interestingly, activation and accumulation of T cells in various nonlymphoid organs was differently affected by lack of IFN-γ, suggesting that IFN-γ influences T cell effector function to varying levels in different anatomical locations. Importantly, control of splenic T cell numbers during P. berghei ANKA infection depended on active IFN-γ-dependent environmental signals--leading to T cell apoptosis--rather than upon intrinsic alterations in T cell programming. To our knowledge, this is the first study to fully investigate the role of IFN-γ in modulating T cell function during P. berghei ANKA infection and reveals that IFN-γ is required for efficient contraction of the pool of activated T cells
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
Pelevin’s Trinity in the novel “t”: author – protagonist – reader
No full textThe article attempts to interpret Pelevin's artistic strategy in the novel "T" by exploring its subject organization and addressing the key problems of the author, the protagonist, and the reader as they are seen by the researcher. The article analyzes the peculiarities of constructing the narrative reality in the novel "T", and goes on to discuss Pelevin's philosophic models of the development of the humankind, and the emergence of his new anthropology
Measuring industry-science links through inventor-author relations: A profiling method
No full textIn this pilot study we examine the performance of text-based profiling in recovering a set of validated inventor-author links. In a first step we match patents and publications solely based on their similarity in content. Next, we compare inventor and author names on the highest ranked matches for the occurrence of name matches. Finally, we compare these candidate matches with the names listed in a validated set of inventor-author names. Our text-based profile methodology performs significantly better than a random matching of patents and publications, suggesting that text-based profiling is a valuable complementary tool to the name searches used in previous studies.innovation; industry-science links; text-based profiling;
Wave turbulence of a rotating array of quantized vortices in the T → 0 temperature limit
No full textThe dynamics of quantized vortices in the zero temperature limit is currently of great interest, particularly in the case of the Fermi superfluid He-B. Here we study wave turbulence, generated by the librating motion of a rotating cylindrical container filled with He-B, in the limit of vanishing viscous forces at temperatures . The polarization of the quantized vortices with respect to the axis of rotation is measured using non-invasive NMR techniques. We observe a decrease of the polarization when the librating motion is started, and a two-stage relaxation process when the modulation of the rotation velocity is stopped. The first relaxation process is associated with the dissipation of large-scale flow stored in inertial waves and the solid body rotation of the vortex array. From the decay of these energy reservoirs we determine the rate of energy dissipation of large-scale flow. The later second process is related to the relaxation of Kelvin waves on individual vortices. This process is monitored by the recovery of the polarization. The existence of a Kelvin wave cascade at the lowest temperatures is currently a central open question. We supply some evidence for the cascade
- …
