Wind turbine drive-train condition monitoring through tower vibrations measurement and processing

A new method for wind turbine drive-train condition monitoring is proposed: the innovative idea is that vibrations are measured at the tower. The critical point is extracting knowledge about the drive-train from tower measurements: this is achieved by measuring simultaneously at the highest possible number of nearby wind turbines. One wind turbine is selected as target and the others are used as reference. The data are analyzed in the time domain basing on statistical features (root mean square, peak, crest factor, skewness, kurtosis). The data set in the feature space reduces to a matrix, from which the observations at the target wind turbine should be distinguishable. The application of this algorithm is supported by univariate statistical tests and by Principal Component Analysis. A novelty index based on the Mahalanobis distance is finally used to detect the statistical novelty of the damaged wind turbine. This work is based on field measurement campaigns, performed by the authors in 2018 and 2019 at wind farms owned by the Renvico company

Diagnosis of faulty wind turbine bearings using tower vibration measurements †

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring

АДАПТИВНЫЕ МИРИАДНЫЕ ФИЛЬТРЫ ДЛЯ ОБРАБОТКИ СИГНАЛОВ ЭЛЕКТРОКАРДИОГРАММЫ, РЕГИСТРИРУЕМЫХ С ВЫСОКОЙ ЧАСТОТОЙ ДИСКРЕТИЗАЦИИ

Предложены локально-адаптивные мириадные фильтры для обработки сигналов ЭКГ, регистрируемых с высокой частотой дискретизации. Разработан локально-адаптивный мириадный фильтр, в котором адаптивно изменяются размер скользящего окна и параметр линейности мириадной оценки, в результате достигаются высокие интегральные показатели эффективности фильтрации. Для тестовых сигналов ЭКГ в широком диапазоне изменения дисперсии аддитивного гауссова шума и при наличии выбросов по критериям среднеквадратической ошибки и отношения сигнал-шум получены статистические оценки качества неадаптивных и адаптивных нелинейных фильтров. Показано существенное преимущество локально-адаптивной мириадной фильтрации. На основании оценок эффективности и обработки модельных и реальных сигналов, с учетом уменьшения вычислительных затрат на программную реализацию, разработанный локально-адаптивный мириадный фильтр рекомендуется для применения в задачах подавления шума в ЭКГ

Locally-adaptive Myriad Filtration of One-dimensional Complex Signal

Locally-adaptive algorithms of myriad filtering are proposed with adaptation of a sample myriad linearity parameter K, depending upon local estimates of a signal, and with “hard” switching of sliding window length settings and a coefficient which influences on the parameter K. Statistical estimates of the filters quality are obtained using a criterion of a minimum mean-square error for a model of one-dimensional complex signal that includes different elementary segments under conditions of additive Gaussian noise with zero mean and different variances and possible spikes presence. Improvement of integral and local performance indicators is shown in comparison to the highly effective non-linear locally-adaptive algorithms for the considered test signal. Having a complex signal of high efficiency, one of the proposed algorithms provides nearly optimal noise suppression at the segments of linear change of a signal; other algorithm provides higher quality of step edge preservation and the best noise suppression on a const signal. Better efficiency in cases of low and high noise levels is achieved by preliminary noise level estimation through comparison of locally-adaptive parameter and thresholds. It is shown that, in order to ensure better spikes removal, it is expedient to pre-process the signal by robust myriad filter with small window length. The considered adaptive nonlinear filters have possibility to be implemented in a real time mode

Deep diving into the S&P Europe 350 index network and its re-action to COVID-19

In this paper, we analyse the dynamic partial correlation network of the constituent stocks of S&P Europe 350. We focus on global parameters such as radius, which is rarely used in financial networks literature, and also the diameter and distance parameters. The first two parameters are useful for deducing the force that economic instability should exert to trigger a cascade effect on the network. With these global parameters, we hone the boundaries of the strength that a shock should exert to trigger a cascade effect. In addition, we analysed the homophilic profiles, which is quite new in financial networks literature. We found highly homophilic relationships among companies, considering firms by country and industry. We also calculate the local parameters such as degree, closeness, betweenness, eigenvector, and harmonic centralities to gauge the importance of the companies regarding different aspects, such as the strength of the relationships with their neighbourhood and their location in the network. Finally, we analysed a network substructure by introducing the skeleton concept of a dynamic network. This subnetwork allowed us to study the stability of relations among constituents and detect a significant increase in these stable connections during the Covid-19 pandemic

On the physical interaction between ocean waves and coastal cliffs

Wave impacts have long been posited as the primary forcing mechanism of coastal cliff recession. Recent developments in the study of hydrodynamics at coastal structures such as seawalls and breakwaters have shown that wave pressures are stochastic in nature and have a broad range of first- and second-order controls. This understanding has yet to be translated to coastal cliffs, where it is still largely assumed that wave impact characteristics can be predicted by simple deterministic formulae. Hydraulic components in coastal models are limited by the lack of in-situ measurements of waves at the cliff toe due to the difficulties in deploying instrumentation in such energetic and inaccessible environments. To address this, I have approached the problem threefold. Monthly high-resolution terrestrial laser scanning (TLS) was undertaken over a year at multiple sites at Staithes, North Yorkshire, to evaluate the recession rate and detachment characteristics of the lower cliff section. Concurrently, wave gauges were deployed at the cliff toe of each site to monitor wave conditions. A novel method of measuring wave impacts was undertaken at one of the sites for nine low-to-low tidal cycles. New and established methods for processing this data were used. Analysis of the erosion dataset revealed distinct temporal patterns of erosion, with accelerated erosion rates during winter. Vertical variations in detachment volumes below 0.1 m3 related to the tidal elevation were also observed, suggesting a key marine influence. Detachment frequency and volume were found to be influenced by lithology type and joint density. Wave conditions over the study period were found to be depth-limited, yet some waves at the toe were found to be larger than those offshore due to shoaling. Wave breaking conditions were strongly influenced by platform morphology and tidal stage. Up to 9% of all waves were breaking on impact. Measurements of wave impacts revealed approximately 14% of wave exhibited high-magnitude impulsive pressures generated by breaking and broken waves. These were analysed probabilistically and found to be controlled primarily by the ratio between wave height and water depth. These data were used to develop a conceptual model of forcing at the cliff toe, including an evaluation of the ability of waves to remove material via enhanced pressure inside discontinuities and fragmentation of weathered material. These results have broad implications concerning the process geomorphology of rock coasts and the evaluation of wave forcing in coastal models

Characterization of Volatile Organic Compound Emissions from Anthropogenic and Emerging Biogenic Sources

Volatile organic compounds are emitted by myriad sources. This thesis investigates trends in emissions on global, regional, and local scales. Globally, an increasing trend in ethane and propane emissions was observed, mainly as a result of oil and natural gas (O&amp;NG;) development. Regionally, air composition varied as a result of the efficacy of emission controls from mobile and industrial sources, and unconventional O&amp;NG; development. Unconventional O&amp;NG; development has also had a demonstrated effect of Colorado&rsquo;s Northern Front Range. An elevation gradient was observed that suggests emissions from metropolitan and O&amp;NG; development centers in this area influence air composition in the adjacent foothills. A spatial gradient of O&amp;NG; tracers was also observed; mixing ratios increased as distance to an area of concentrated O&amp;NG; development decreased. Given the ever increasing proximity of O&amp;NG; emissions to population centers, concerned citizens desire a method to assess air quality in and around their homes, schools, and offices. An affordable method for measuring C3-C5 alkanes was developed utilizing passive adsorbent sampling cartridges, though further experimentation is needed to determine the absolute accuracy of these devices. Finally, VOC emissions from soil and bacteria are characterized. Soil VOC emissions mirrored the &ldquo;Birch Effect&rdquo;, and spiked following a wetting event. Bacterial VOC emission profiles displayed strong taxonomic and phylogenetic signals, and suggest VOC play a role in finer-scale patterns of ecological diversity.</p

Generalized Hampel Filters

The standard median filter based on a symmetric moving window has only one tuning parameter: the window width. Despite this limitation, this filter has proven extremely useful and has motivated a number of extensions: weighted median filters, recursive median filters, and various cascade structures. The Hampel filter is a member of the class of decsion filters that replaces the central value in the data window with the median if it lies far enough from the median to be deemed an outlier. This filter depends on both the window width and an additional tuning parameter t, reducing to the median filter when t=0, so it may be regarded as another median filter extension. This paper adopts this view, defining and exploring the class of generalized Hampel filters obtained by applying the median filter extensions listed above: weighted Hampel filters, recursive Hampel filters, and their cascades. An important concept introduced here is that of an implosion sequence, a signal for which generalized Hampel filter performance is independent of the threshold parameter t. These sequences are important because the added flexibility of the generalized Hampel filters offers no practical advantage for implosion sequences. Partial characterization results are presented for these sequences, as are useful relationships between root sequences for generalized Hampel filters and their median-based counterparts. To illustrate the performance of this filter class, two examples are considered: one is simulation-based, providing a basis for quantitative evaluation of signal recovery performance as a function of t, while the other is a sequence of monthly Italian industrial production index values that exhibits glaring outliers.Peer reviewe

Generalized Hampel Filters

The standard median filter based on a symmetric moving window has only one tuning parameter: the window width. Despite this limitation, this filter has proven extremely useful and has motivated a number of extensions: weighted median filters, recursive median filters, and various cascade structures. The Hampel filter is a member of the class of decsion filters that replaces the central value in the data window with the median if it lies far enough from the median to be deemed an outlier. This filter depends on both the window width and an additional tuning parameter t, reducing to the median filter when t=0, so it may be regarded as another median filter extension. This paper adopts this view, defining and exploring the class of generalized Hampel filters obtained by applying the median filter extensions listed above: weighted Hampel filters, recursive Hampel filters, and their cascades. An important concept introduced here is that of an implosion sequence, a signal for which generalized Hampel filter performance is independent of the threshold parameter t. These sequences are important because the added flexibility of the generalized Hampel filters offers no practical advantage for implosion sequences. Partial characterization results are presented for these sequences, as are useful relationships between root sequences for generalized Hampel filters and their median-based counterparts. To illustrate the performance of this filter class, two examples are considered: one is simulation-based, providing a basis for quantitative evaluation of signal recovery performance as a function of t, while the other is a sequence of monthly Italian industrial production index values that exhibits glaring outliers.Peer reviewe