Imaging time series for the classification of EMI discharge sources

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome

Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Preconditioning and triggering of offshore slope failures and turbidity currents revealed by most detailed monitoring yet at a fjord-head delta

Rivers and turbidity currents are the two most important sediment transport processes by volume on Earth. Various hypotheses have been proposed for triggering of turbidity currents offshore from river mouths, including direct plunging of river discharge, delta mouth bar flushing or slope failure caused by low tides and gas expansion, earthquakes and rapid sedimentation. During 2011, 106 turbidity currents were monitored at Squamish Delta, British Columbia. This enables statistical analysis of timing, frequency and triggers. The largest peaks in river discharge did not create hyperpycnal flows. Instead, delayed delta-lip failures occurred 8–11 h after flood peaks, due to cumulative delta top sedimentation and tidally-induced pore pressure changes. Elevated river discharge is thus a significant control on the timing and rate of turbidity currents but not directly due to plunging river water. Elevated river discharge and focusing of river discharge at low tides cause increased sediment transport across the delta-lip, which is the most significant of all controls on flow timing in this setting

The Age and Structure of the Galactic Bulge from Mira Variables

We report periods and JHKL observations for 648 oxygen-rich Mira variables found in two outer bulge fields at b=-7 degrees and l=+/-8 degrees and combine these with data on 8057 inner bulge Miras from the OGLE, Macho and 2MASS surveys, which are concentrated closer to the Galactic centre. Distance moduli are estimated for all these stars. Evidence is given showing that the bulge structure is a function of age. The longer period Miras (log P > 2.6, age about 5 Gyr and younger) show clear evidence of a bar structure inclined to the line of sight in both the inner and outer regions. The distribution of the shorter period (metal-rich globular cluster age) Miras, appears spheroidal in the outer bulge. In the inner region these old stars are also distributed differently from the younger ones and possibly suggest a more complex structure. These data suggest a distance to the Galactic centre, R0, of 8.9 kpc with an estimated uncertainty of 0.4 kpc. The possible effect of helium enrichment on our conclusions is discussed.Comment: Accepted for MNRAS, 12 pages, 12 figure

Promoting Adaptive Performance through Learner-Controlled Practice Difficulty and Individualized Challenge: A Latent Growth Modeling Approach

In any learner-controlled, active learning environment, the choices one makes can influence both the objective training difficulty and individualized levels of trainee challenge faced during learning. Despite research suggesting that certain difficulties experienced while learning can be beneficial for promoting knowledge, skill, and transfer (R. A. Schmidt & Bjork, 1992), the roles of learner-controlled practice difficulty and associated levels of individualized challenge are not well understood. Moreover, research has yet to examine empirically the nature of the cause-and-effect relationships between active learning behaviors and related psychological processes, and single measures of adaptive transfer are typically used despite the multidimensional nature of training transfer (Barnett & Ceci, 2002). Therefore, the present study examined these issues by giving 152 male participants control over their practice difficulty operationalized in terms of objective levels of task complexity while playing a complex videogame. Results revealed that metacognition and self-efficacy each exhibited positive influences on learner-controlled practice difficulty. Furthermore, both the overall average level and growth of practice difficulty had positive relationships with basic knowledge and post-training performance. The overall average level of practice difficulty was also positively related to strategic knowledge. Conversely, growth of individualized challenge had negative relationships with knowledge and post-training performance. In turn, post-training performance mediated the influences of difficulty and challenge on three distinct types of adaptive transfer performance. Findings are discussed with respect to the beneficial role of practice difficulty during training as well as the need to use multidimensional assessments of transfer outcomes