Thematische Bibliographien / Non-Stationary conditions

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Auswahl der wissenschaftlichen Literatur zum Thema „Non-Stationary conditions“

Autor: Grafiati

Veröffentlicht am 5. April 2025

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Zeitschriftenartikel zum Thema "Non-Stationary conditions"

Abu el Ata-Doss, S., und P. Ponty. „Supervision of Controlled Processes in Non-Stationary Conditions“. IFAC Proceedings Volumes 18, Nr. 5 (Juli 1985): 351–56. http://dx.doi.org/10.1016/s1474-6670(17)60584-6.

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Verhoeven, Marijn J. M., und Harrie A. A. Verbon. „Expectations on pension schemes under non-stationary conditions“. Economics Letters 36, Nr. 1 (Mai 1991): 99–103. http://dx.doi.org/10.1016/0165-1765(91)90063-q.

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Čiegis, Raimondas, Olga Štikonienė und Olga Suboč. „On one problem with non-local boundary conditions“. Lietuvos matematikos rinkinys 41 (17.12.2001): 497–503. http://dx.doi.org/10.15388/lmr.2001.34636.

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In this article two problems with non-local boundary conditions are analysed: stationary diffusion problem with constant coeficients and non-local boundary condition, and stationary diffusion problem with additional convection term and non-local boundary condition. Finite difference schemes are constructed and investigated. The sufficient conditions for the existence of an unique solution are obtained.

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Vogel, Richard M., und Charles N. Kroll. „A comparison of estimators of the conditional mean under non-stationary conditions“. Advances in Water Resources 143 (September 2020): 103672. http://dx.doi.org/10.1016/j.advwatres.2020.103672.

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Ganguli, Rajive, und Jon C. Yingling. „Algorithms to control coal segregation under non-stationary conditions“. International Journal of Mineral Processing 61, Nr. 4 (April 2001): 261–71. http://dx.doi.org/10.1016/s0301-7516(00)00063-6.

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Ganguli, Rajive, und Jon C. Yingling. „Algorithms to control coal segregation under non-stationary conditions“. International Journal of Mineral Processing 61, Nr. 4 (April 2001): 241–59. http://dx.doi.org/10.1016/s0301-7516(00)00064-8.

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Murari, A., M. Lungaroni, M. Gelfusa, E. Peluso und J. Vega. „Adaptive learning for disruption prediction in non-stationary conditions“. Nuclear Fusion 59, Nr. 8 (04.07.2019): 086037. http://dx.doi.org/10.1088/1741-4326/ab1ecc.

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Aliyev, A. M., A. R. Safarov, I. V. Balayev, I. I. Osmanova, A. M. Guseynova und F. G. Bayramov. „DEVELOPMENT OF A NON-STATIONARY MATHEMATICAL MODEL FOR THE PROCESS OF POLYMERIZATION OF PROPYLENE“. Azerbaijan Chemical Journal, Nr. 4 (12.12.2020): 6–11. http://dx.doi.org/10.32737/0005-2531-2020-4-6-11.

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The mathematical model has been developed for the process polymerization propylene proceeding under unsteady conditions due to the toxic effect of methylacetylene on it, leading to decrease of the productivity and quality of polypropylene. Non-stationary function to maintain the productivity at the optimum level obtained during the process in stationary conditions has been proposed. Using this mathematical model allow ones control the process, stabilize it at any time of the polymerization operation. The control scheme of algorithm of this process has been created

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Kukačka, Leoš, Jan Kraus, Milan Kolář, Pascal Dupuis und Georges Zissis. „Review of AC power theories under stationary and non-stationary, clean and distorted conditions“. IET Generation, Transmission & Distribution 10, Nr. 1 (07.01.2016): 221–31. http://dx.doi.org/10.1049/iet-gtd.2015.0713.

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Himani und Ratna Dahiya. „Condition monitoring of wind turbine for rotor fault detection under non stationary conditions“. Ain Shams Engineering Journal 9, Nr. 4 (Dezember 2018): 2441–52. http://dx.doi.org/10.1016/j.asej.2017.04.002.

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Dissertationen zum Thema "Non-Stationary conditions"

Rajagopalan, Satish. „Detection of Rotor and Load Faults in BLDC Motors Operating Under Stationary and Non-Stationary Conditions“. Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11524.

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Brushless Direct Current (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are being increasingly used in critical high performance industries such as appliances, automotive, aerospace, consumer, medical, industrial automation equipment and instrumentation. Fault detection and condition monitoring of BLDC machines is therefore assuming a new importance. The objective of this research is to advance the field of rotor and load fault diagnosis in BLDC machines operating in a variety of operating conditions ranging from constant speed to continuous transient operation. This objective is addressed as three parts in this research. The first part experimentally characterizes the effects of rotor faults in the stator current and voltage of the BLDC motor. This helps in better understanding the behavior of rotor defects in BLDC motors. The second part develops methods to detect faults in loads coupled to BLDC motors by monitoring the stator current. As most BLDC applications involve non-stationary operating conditions, the diagnosis of rotor faults in non-stationary conditions forms the third and most important part of this research. Several signal processing techniques are reviewed to analyze non-stationary signals. Three new algorithms are proposed that can track and detect rotor faults in non-stationary or transient current signals.

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Guan, Yunpeng. „Velocity Synchronous Approaches for Planetary Gearbox Fault Diagnosis under Non-Stationary Conditions“. Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38636.

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Time-frequency methods are widely used tools to diagnose planetary gearbox fault under non-stationary conditions. However, the existing time-frequency methods still have some problems, such as smearing effect and cross-term interference, and these problems limit the effectiveness of the existing time-frequency methods in planetary gearbox fault diagnosis under non-stationary conditions. To address the aforementioned problems, four time-frequency methods are proposed in this thesis. As nowadays a large portion of the industrial equipment is equipped with tachometers, the first three methods are for the cases that the shaft rotational speed is easily accessible and the last method is for the cases of shaft rotational speed is not easily accessible. The proposed methods are itemized as follows: (1) The velocity synchronous short-time Fourier transform (VSSTFT), which is a type of linear transform based on the domain mappings and short-time Fourier transform to address the smear effect of the existing linear transforms under known time-varying speed conditions; (2) The velocity synchrosqueezing transform (VST), which is a type of remapping method based on the domain mapping and synchrosqueezing transform to address the smear effect of existing remapping methods under known time-varying speed conditions; (3) The velocity synchronous bilinear distribution (VSBD), which is a type of bilinear distribution based on the generalized demodulation and Cohen’s class bilinear distribution to address the smear effect and cross-term interference of existing bilinear distributions under known time-varying speed conditions and (4) The velocity synchronous linear chirplet transform (VSLCT), which is a non-parametric combined approach of linear transform and concentration-index-guided parameter determination to provide a smear-free and cross-term-free TFR under unknown time-varying speed conditions. In this work, simple algorithms are developed to avoid the signal resampling process required by the domain mappings or demodulations of the first three methods (i.e., the VSSTFT, VST and VSBD). They are designed to have different resolutions, readabilities, noise tolerances and computational efficiencies. Therefore, they are capable to adapt different application conditions. The VSLCT, as a kind of linear transform, is designed for unknown rotational speed conditions. It utilizes a set of shaft-rotational-speed-synchronous bases to address the smear problem and it is capable to dynamically determine the signal processing parameters (i.e., window length and normalized angle) to provide a clear TFR with desirable time-frequency resolution in response to condition variations. All of the proposed methods in this work are smear-free and cross-term-free, the TFRs generated by the methods are clearer and more precise compared with the existing time-frequency methods. The faults of planetary gearboxes, if any, can be diagnosed by identifying the fault-induced components from the obtained TFRs. The four methods are all newly applied to fault diagnosis. The effectiveness of them has been validated using both simulated and experimental vibration signals of planetary gearboxes collected under non-stationary conditions.

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Baggerohr, Stephan. „A deep learning approach towards diagnostics of bearings operating under non-stationary conditions“. Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73452.

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Faults in bearings usually manifest as marginal defects that intensify over time, allowing for well-informed preventative actions with early Fault Detection and Diagnosis (FDD) protocols. Detection of the fault begins with capturing, for example, acceleration signals from a machine. Traditionally, handpicked descriptive statistical features (mean, RMS, skewness, kurtosis, etc.) or spectral diagrams obtained from these signals are then used for FDD. However, machine signals are often generated under non-stationary operating conditions of varying loads and speeds, requiring further intervention. More advanced signal processing techniques (spectral kurtosis, or cyclostationary analysis) are hence used to account for the non-stationarity of the signal. This is usually done by separating acceleration signals into deterministic and random components. Fault detection in bearings is possible by observing the random components of the signal. A wealth of research has been invested in machine learning-based techniques to circumvent the problems associated with non-stationary signals. Many of these methods require vast amounts of historical data to train. Machines typically spend most of their life operating in a healthy condition, therefore, most historical data is occupied with data that comes from a healthy machine condition, training these methods is difficult, due to the shortage of data from a machine running in an unhealthy condition. Furthermore, well-performing machine learning algorithms still require a domain expert to extract features that are known to be fault sensitive. Deep learning is a recent approach in data analysis whereby feature extraction is incorporated within the training of the algorithm. The algorithm is given the ability to find and extract its features. The architecture of the algorithm allows for the extraction of complex hierarchical non-linear features. To the author’s knowledge, no attempt has been made to make full use of the power of deep learning together with the known structure of bearing acceleration signals to perform FDD. In this work, a bearing FDD methodology is developed using deep learning approaches. A model based on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) is used to learn a lower-dimensional representation of an acceleration signal. A regularization strategy based on information maximization is used, which allows deterministic and random components of the signals to be learned separately. This representation is subsequently used to perform bearing FDD. The algorithm is trained in a completely unsupervised manner on exclusively healthy data and requires no preprocessing of that data. Furthermore, no auxiliary signals such as a shaft encoder, which contains information about the machine operating condition, is required for the algorithm to work. The methodology was tested on well-known benchmark datasets, and it was shown to be robust against non-stationary operating conditions. The algorithm can learn its fault metric and by observing the trajectory of the signal representation, it is also able to diagnose the type of fault.
Dissertation (MEng)--University of Pretoria, 2019.
Mechanical and Aeronautical Engineering
MEng
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Vedreño, Santos Francisco Jose. „Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions“. Doctoral thesis, Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/34177.

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Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable. Sin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas. La presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta. El diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador). El desarrollo de la metodología conlleva las siguientes etapas: (i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina. (ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas. (iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia. (iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert. (v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales. (vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta. La metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas.
Vedreño Santos, FJ. (2013). Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34177
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Schmidt, Stephan. „A cost-effective diagnostic methodology using probabilistic approaches for gearboxes operating under non-stationary conditions“. Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/61332.

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Condition monitoring is very important for critical assets such as gearboxes used in the power and mining industries. Fluctuating operating conditions are inevitable for wind turbines and mining machines such as bucket wheel excavators and draglines due to the continuous uctuating wind speeds and variations in ground properties, respectively. Many of the classical condition monitoring techniques have proven to be ine ective under uctuating operating conditions and therefore more sophisticated techniques have to be developed. However, many of the signal processing tools that are appropriate for uctuating operating conditions can be di cult to interpret, with the presence of incipient damage easily being overlooked. In this study, a cost-e ective diagnostic methodology is developed, using machine learning techniques, to diagnose the condition of the machine in the presence of uctuating operating conditions when only an acceleration signal, generated from a gearbox during normal operation, is available. The measured vibration signal is order tracked to preserve the angle-cyclostationary properties of the data. A robust tacholess order tracking methodology is proposed in this study using probabilistic approaches. The measured vibration signal is order tracked with the tacholess order tracking method (as opposed to computed order tracking), since this reduces the implementation and the running cost of the diagnostic methodology. Machine condition features, which are sensitive to changes in machine condition, are extracted from the order tracked vibration signal and processed. The machine condition features can be sensitive to operating condition changes as well. This makes it difficult to ascertain whether the changes in the machine condition features are due to changes in machine condition (i.e. a developing fault) or changes in operating conditions. This necessitates incorporating operating condition information into the diagnostic methodology to ensure that the inferred condition of the machine is not adversely a ected by the uctuating operating conditions. The operating conditions are not measured and therefore representative features are extracted and modelled with a hidden Markov model. The operating condition machine learning model aims to infer the operating condition state that was present during data acquisition from the operating condition features at each angle increment. The operating condition state information is used to optimise robust machine condition machine learning models, in the form of hidden Markov models. The information from the operating condition and machine condition models are combined using a probabilistic approach to generate a discrepancy signal. This discrepancy signal represents the deviation of the current features from the expected behaviour of the features of a gearbox in a healthy condition. A second synchronous averaging process, an automatic alarm threshold for fault detection, a gear-pinion discrepancy distribution and a healthy-damaged decomposition of the discrepancy signal are proposed to provide an intuitive and robust representation of the condition of the gearbox under uctuating operating conditions. This allows fault detection, localisation as well as trending to be performed on a gearbox during uctuating operation conditions. The proposed tacholess order tracking method is validated on seven datasets and the fault diagnostic methodology is validated on experimental as well as numerical data. Very promising results are obtained by the proposed tacholess order tracking method and by the diagnostic methodology.
Dissertation (MEng)--University of Pretoria, 2016.
Mechanical and Aeronautical Engineering
MEng
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Zaikou, Yahor [Verfasser], Reiner [Akademischer Betreuer] Thomä, Dusan Gutachter] Kocur und Uwe [Gutachter] [Pliquett. „Microwave UWB sensors for measurements under non-stationary conditions : detection of human being beneath rubble for rescue applications / Yahor Zaikou ; Gutachter: Dusan Kocur, Uwe Pliquett ; Betreuer: Reiner Thomä“. Ilmenau : TU Ilmenau, 2018. http://d-nb.info/1178129004/34.

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凌仕卿 und Shiqing Ling. „Stationary and non-stationary time series models with conditional heteroscedasticity“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31236005.

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Ling, Shiqing. „Stationary and non-stationary time series models with conditional heteroscedasticity /“. Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18611953.

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Barbini, Leonardo. „Techniques for condition monitoring using cyclo-non-stationary signals“. Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.761025.

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Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.

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Fralix, Brian Haskel. „Stability and Non-stationary Characteristics of Queues“. Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14569.

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We provide contributions to two classical areas of queueing. The first part of this thesis focuses on finding new conditions for a Markov chain on a general state space to be Harris recurrent, positive Harris recurrent or geometrically ergodic. Most of our results show that establishing each property listed above is equivalent to finding a good enough feasible solution to a particular optimal stopping problem, and they provide a more complete understanding of the role Foster's criterion plays in the theory of Markov chains. The second and third parts of the thesis involve analyzing queues from a transient, or time-dependent perspective. In part two, we are interested in looking at a queueing system from the perspective of a customer that arrives at a fixed time t. Doing this requires us to use tools from Palm theory. From an intuitive standpoint, Palm probabilities provide us with a way of computing probabilities of events, while conditioning on sets of measure zero. Many studies exist in the literature that deal with Palm probabilities for stationary systems, but very few treat the non-stationary case. As an application of our main results, we show that many classical results from queueing (in particular ASTA and Little's law) can be generalized to a time-dependent setting. In part three, we establish a continuity result for what we refer to as jump processes. From a queueing perspective, we basically show that if the primitives and the initial conditions of a sequence of queueing processes converge weakly, then the corresponding queue-length processes converge weakly as well in some sense. Here the notion of convergence used depends on properties of the limiting process, therefore our results generalize classical continuity results that exist in the literature. The way our results can be used to approximate queueing systems is analogous to the way phase-type random variables can be used to approximate other types of random variables.

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Bücher zum Thema "Non-Stationary conditions"

Fakhfakh, Tahar, Walter Bartelmus, Fakher Chaari, Radoslaw Zimroz und Mohamed Haddar, Hrsg. Condition Monitoring of Machinery in Non-Stationary Operations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8.

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Fernandez Del Rincon, Alfonso, Fernando Viadero Rueda, Fakher Chaari, Radoslaw Zimroz und Mohamed Haddar, Hrsg. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2.

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Timofiejczuk, Anna, Fakher Chaari, Radoslaw Zimroz, Walter Bartelmus und Mohamed Haddar, Hrsg. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61927-9.

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Dalpiaz, Giorgio, Riccardo Rubini, Gianluca D'Elia, Marco Cocconcelli, Fakher Chaari, Radoslaw Zimroz, Walter Bartelmus und Mohamed Haddar, Hrsg. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39348-8.

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Chaari, Fakher, Radoslaw Zimroz, Walter Bartelmus und Mohamed Haddar, Hrsg. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20463-5.

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Fakhfakh, Tahar. Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Second International Conference "Condition Monitoring of Machinery in Non-Stationnary Operations" CMMNO’2012. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Mann, Peter. The Stationary Action Principle. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0007.

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This crucial chapter focuses on the stationary action principle. It introduces Lagrangian mechanics, using first-order variational calculus to derive the Euler–Lagrange equation, and the inverse problem is described. The chapter then considers the Ostrogradsky equation and discusses the properties of the extrema using the second-order variation to the action. It then discusses the difference between action functions (of Dirichlet boundary conditions) and action functionals of the extremal path. The different types of boundary conditions (Dirichlet vs Neumann) are elucidated. Topics discussed include Hessian conditions, Douglas’s theorem, the Jacobi last multiplier, Helmholtz conditions, Noether-type variation and Frenet–Serret frames, as well as concepts such as on shell and off shell. Actions of non-continuous extremals are examined using Weierstrass–Erdmann corner conditions, and the action principle is written in the most general form as the Hamilton–Suslov principle. Important applications of the Euler–Lagrange formulation are highlighted, including protein folding.

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Springer. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Springer London, Limited, 2013.

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Staff, IEEE. 2021 7th International Conference on Condition Monitoring of Machinery in Non Stationary Operations (CMMNO). IEEE, 2021.

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Staff, IEEE. 2021 7th International Conference on Condition Monitoring of Machinery in Non Stationary Operations (CMMNO). IEEE, 2021.

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Buchteile zum Thema "Non-Stationary conditions"

Benvenuti, A., G. Ghione und C. U. Naldi. „Non-Stationary Transport HBT Modeling Under Non-Isothermal Conditions“. In Simulation of Semiconductor Devices and Processes, 453–56. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-6657-4_112.

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Strozzi, Matteo, Riccardo Rubini und Marco Cocconcelli. „Condition Monitoring Techniques of Ball Bearings in Non-stationary Conditions“. In Lecture Notes in Mechanical Engineering, 565–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31154-4_48.

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Hunter, John, Simon P. Burke und Alessandra Canepa. „Testing for Cointegration: Standard and Non-Standard Conditions“. In Multivariate Modelling of Non-Stationary Economic Time Series, 145–204. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-31303-4_4.

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Kępski, Paweł, und Tomasz Barszcz. „Application of Vibration Monitoring for Mining Machinery in Varying Operational Conditions“. In Condition Monitoring of Machinery in Non-Stationary Operations, 461–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_48.

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Czop, Piotr, Tomasz Barszcz und Jarosław Bednarz. „Adjustment of a Feedwater Heater Model in Bi-stationary Load Conditions“. In Condition Monitoring of Machinery in Non-Stationary Operations, 573–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_59.

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Heyns, Theo, und Stephan Heyns. „Gear Fault Detection under Fluctuating Operating Conditions by Means of Discrepancy Analysis“. In Condition Monitoring of Machinery in Non-Stationary Operations, 81–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_9.

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Sghaier, Emna, Adeline Bourdon, Didier Remond, Jean-Luc Dion und Nicolas Peyret. „Non-stationary Operating Conditions of Rotating Machines: Assumptions and Their Consequences“. In Applied Condition Monitoring, 11–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_2.

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Khranovich, I. L., und A. L. Velikanov. „Management of Water Resources Systems Under Non-Stationary Conditions“. In Hydrological Models for Environmental Management, 157–68. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0470-1_13.

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Chaari, Fakher, und Mohamed Haddar. „Modeling of Gear Transmissions Dynamics in Non-stationary Conditions“. In Lecture Notes in Mechanical Engineering, 109–24. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04187-2_8.

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Dolenc, Boštjan, Pavle Boškoski und Ðani Juričić. „Robust Information Indices for Diagnosing Mechanical Drives Under Non-stationary Operating Conditions“. In Applied Condition Monitoring, 139–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_11.

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Konferenzberichte zum Thema "Non-Stationary conditions"

Cherneva, Z., und C. Guedes Soares. „Local Non-Stationary Properties of Wind Wave Groups“. In Design and operation For Abnormal Conditions 2. RINA, 2001. http://dx.doi.org/10.3940/rina.aco.2001.9.

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Zubizarreta-Rodriguez, Jose F., und Shrihari Vasudevan. „Condition monitoring of brushless DC motors with non-stationary dynamic conditions“. In 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2014. http://dx.doi.org/10.1109/i2mtc.2014.6860523.

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Reshetnikova, N. V., und E. Y. Vataeva. „METHODS OF ACS RESEARCH UNDER NON-STATIONARY CONDITIONS“. In ZAVALISHENSKY READING’20. St. Petersburg State University of Aerospace Instrumentation, 2020. http://dx.doi.org/10.31799/978-5-8088-1446-2-2020-15-148-151.

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Esteves, Jose Jurandir Alves, Amina Boubendir, Fabrice Guillemin und Pierre Sens. „DRL-based Slice Placement Under Non-Stationary Conditions“. In 2021 17th International Conference on Network and Service Management (CNSM). IEEE, 2021. http://dx.doi.org/10.23919/cnsm52442.2021.9615543.

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Alippi, C., und M. Roveri. „Just-in-time Adaptive Classifiers in Non-Stationary Conditions“. In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371097.

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Mouapi, Alex, Nadir Hakem und Nahi Kandil. „Characterization a Rectifying Antenna under Non-Stationary Propagation Conditions“. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2020. http://dx.doi.org/10.1109/eeeic/icpseurope49358.2020.9160497.

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Wang, Chao, Kewei Wu, Zhansi Jiang, Zhongyi Song und Shuanhu Wu. „Detection and diagnosis of fault roller bearings under variable speed conditions“. In 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO). IEEE, 2021. http://dx.doi.org/10.1109/cmmno53328.2021.9467608.

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Dekusha, Leonid, Svitlana Kovtun und Oleg Dekusha. „Heat Flux Control in Non-stationary Conditions for Industry Applications“. In 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, 2019. http://dx.doi.org/10.1109/ukrcon.2019.8879847.

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Hechavarría, Rodney, und Diana Coello-Fiallos. „Solving the heat equation problem under periodic non-stationary conditions“. In PROCEEDINGS OF THE 2ND INTERNATIONAL CONGRESS ON PHYSICS ESPOCH (ICPE-2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5050359.

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Panigrahi, B. K., und S. K. Sinha. „Detection and Classification of Non-stationary Power disturbances in Noisy Conditions“. In 2006 International Conference on Power Electronic, Drives and Energy Systems. IEEE, 2006. http://dx.doi.org/10.1109/pedes.2006.344258.

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Berichte der Organisationen zum Thema "Non-Stationary conditions"

Melo-Velandia, Luis Fernando, Camilo Andrés Orozco-Vanegas und Daniel Parra-Amado. Extreme weather events and high Colombian food prices: A non-stationary extreme value approach. Banco de la República, Dezember 2021. http://dx.doi.org/10.32468/be.1189.

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Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Ni˜no.We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is non linear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Ni˜no in 1988 and 2016 are expected to reoccur once every 50 years.

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Bacon und Olsen. PR-179-13202-R01 Field Evaluation of a Continental Controls Corp. NSCR NOx Sensor Control System. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), Juni 2014. http://dx.doi.org/10.55274/r0010203.

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Emissions compliance of stationary engines can be successful with the application of a non-selective catalytic reduction (NSCR) after treatment system. To accomplish this, the equivalence ratio (?) must be precisely controlled within a narrow range near stoichiometric conditions. The ability for Air Fuel Ratio (AFR) control systems to maintain the engine equivalence ratio in the required narrow operating range long term under field conditions has not been established. This project builds upon prior work at the Colorado State University (CSU) Engines and Energy Conversion Laboratory (EECL) to develop a NOx sensor minimization control algorithm utilizing an AFR controller manufactured by Continental Controls Corporation. Testing was performed on a Waukesha VGF-series L36GSI engine utilized for power generation.

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Yeates, Elissa, Kayla Cotterman und Angela Rhodes. Hydrologic impacts on human health : El Niño Southern Oscillation and cholera. Engineer Research and Development Center (U.S.), Januar 2020. http://dx.doi.org/10.21079/11681/39483.

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A non-stationary climate imposes considerable challenges regarding potential public health concerns. The El Niño Southern Oscillation (ENSO) cycle, which occurs every 2 to 7 years, correlates positively with occurrences of the waterborne disease cholera. The warm sea surface temperatures and extreme weather associated with ENSO create optimal conditions for breeding the Vibrio cholerae pathogen and for human exposure to the pathogenic waters. This work explored the impacts of ENSO on cholera occurrence rates over the past 50 years by examining annual rates of suspected cholera cases per country in relation to ENSO Index values. This study provides a relationship indicating when hydrologic conditions are optimal for cholera growth, and presents a statistical approach to answer three questions: Are cholera outbreaks more likely to occur in an El Niño year? What other factors impact cholera outbreaks? How will the future climate impact cholera incidence rates as it relates to conditions found in ENSO? Cholera outbreaks from the 1960s to the present are examined focusing on regions of Central and South America, and southern Asia. By examining the predictive relationship between climate variability and cholera, we can draw conclusions about future vulnerability to cholera and other waterborne pathogenic diseases.

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