Wyniki 1-2 spośród 2 dla zapytania: authorDesc:"Michał DOŁĘGOWSKI"

Mechanisms of electric arc detection based on current waveform spectrum and incremental decomposition analysis DOI:10.15199/48.2016.11.15

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This paper presents two methods that enable arc detection, these are frequency analysis FFT and incremental decomposition of current increase in time. Both methods enable arc detection and provide additional information as compared to currently existing commercial solutions. Streszczenie. W artykule zaprezentowano dwie metody umożliwiające wykrywanie występowania łuku elektrycznego: analizę częstotliwościową FFT oraz metodę badania rozkładu przyrostów prądu w czasie. Obydwie metody umożliwiają detekcję łuku oraz dostarczają dodatkowych informacji w stosunku do istniejących obecnie rozwiązań komercyjnych. (Metody detekcji łuku elektrycznego w oparciu o analizę widmową oraz przyrostową przebiegu prądowego). Keywords: electric arc, signal processing, spectrum analysis, incremental decomposition. Słowa kluczowe: łuk elektryczny, przetwarzanie sygnałów, analiza widmowa, analiza przyrostowa. Introduction Formation of electric arc is a highly disadvantageous phenomenon that appears increasingly in electric installations. This phenomenon can occur in both the renewable energy installations [1÷5] and in the railway electric traction [6÷9]. The negative effects of electric arc can be experienced in households, where micro-power photovoltaic or wind energy systems have been used, as well as in industrial power systems [10÷16]. On the basis of the above, it can be concluded that the scale of this phenomenon is very extensive. In order to protect electrical installations from occurrence of electric arc, methods based on detection of sudden voltage drop and light-emission of electric arc are currently applied. Further, research is being carried out into current waveforms spectral analysis [17], which provides additional information on the load nature and the distortion degree of nonlinear receivers’ network. Some kind of analyses might be taken from power quality measuring methodology [18]. In addition, studies are conducted into altern[...]

Use of incremental decomposition and spectrogram in vibroacoustic signal analysis in knee joint disease examination DOI:10.15199/48.2018.07.41

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Joint dysfunctions are the most common disorders in human locomotor system. Complex biomechanical environment makes the knee joint particularly susceptible to such disorders [5]. Although X-ray has a number of advantages and is still a basic form of assessment of the articular system, this method presents some limitations that significantly reduce its clinical relevance [6]. On the other hand, access to more sensitive and specific imaging methods (e.g. MRI) is significantly limited due to high costs. In addition, it should be noted that these methods allow only for structure evaluation, without the assessment of the joint function [1]. Vibroarthrography (VAG) is an experimental method for assessing the function of articular structures. This method bases on recording mechanical vibrations generated by moving articular cartilaginous surfaces, which under physiological conditions reduce the coefficient of friction [2, 3]. However, in the course of various disorders within the articular system the cartilage structures are damaged or the changes in their biomechanical characteristics appear. This results in increased friction and increased vibrations corresponding to the degree of damage to the joint [2]. Because of its non-invasive character and relatively low cost, VAG method appears to be a promising diagnostic tool that can be used in clinical conditions. However, it is necessary to develop algorithms that classify individual disorders with respect to generally accepted diagnostic criteria. Therefore, the aim of this paper is to use the incremental decomposition and spectrograms in the analysis of vibroarthrography signals representing different levels of cartilaginous disorders within the knee joint. Measurement methodology The VAG signal was recorded using the 4513B-002 accelerometer sensor and the Brüel & Kjær Nexus 2692-C signal amplifier connected to an analogue-to-digital converter and a PC. The sensor was attac[...]

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