The article proposes an adaptive algorithm that generates all object signals, including those for which measurements are not performed due to the difficulties associated with on-line measurements. The algorithm is modeled on the idea of the Kalman filter using its equation, however, the selection of gains is optimized in a different way, i.e. the constant values depend on the adopted ranges of adaptation errors. Moreover, the knowledge of the statistics of all noise signals is not imposed and there is no linearity constraint. This approach allowed to reduce the complexity of calculations. This algorithm can be used in real-time systems to generate signals of objects described by non-linear differential equations and it is universal, which allows it to be used for various objects. In the conducted research, on the example of a biochemically contaminated river, only easily measurable signals were used to generated the object signals, and in addition, in the case of absence some measurements, the functioning of the algorithm did not destabilize.
The paper shows the degradation process of the modern austenitic Super 304H (X10CrNiCuNb18-9-3) steel which was subjected to long-term aging for up to 50,000 h at 650 and 700°C. The investigations include microstructure examination (SEM), identification and analysis of the precipitation process, and mechanical properties tests. The Super 304H steel has a structure characteristic of austenitic steels with visible annealing twins and single primary NbX precipitates. Long-term aging in the steel leads to numerous precipitation processes of M23C6, MX carbides, σ phase, Z phase, and -Cu phase. Precipitation processes lead to a decrease in plastic properties and impact energy as well as alloy over aging. Yield strength and tensile strength values after 50,000 h of aging were similar to those as delivered. The yield and tensile strength value strongly depend on the applied aging temperature.
The paper presents stress-strain characteristics recorded during the four-step compression of axisymmetric samples in the Gleeble thermomechanical simulator. The hot deformability of three steels with Mn concentrations of 3%, 4% and 5% was compared. The analysis of the influence of plastic deformation and Mn content on the microstructure of alloys, and in particular, on a fraction and morphological features of the retained austenite, was performed. The proportion of the retained austenite was determined by the X-ray diffraction method. It was found that the content of Mn in the range from 3% to 5% does not have a significant impact on the high-temperature resistance of the steel during compression tests, but it has a significant influence on the microstructure of the steel and the fraction of retained austenite. The optimal conditions for maximizing the proportion of retained austenite were obtained at the temperature of 400 °C, and it decreased with increasing Mn concentration in the steel. It has been shown that it is related to the redistribution of carbon from the remaining austenite fraction with an increase in the manganese content. The mechanical properties were determined on the basis of hardness measurements.
Silesian University of Technology, Faculty of Mechanical Engineering, Department of Engineering Materials and Biomaterials, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Cermet coatings provide protection against aggressive operating environment of machine and device elements, such as corrosion, wear or high-temperature conditions. Currently WC-based cermet coatings are frequently used in the different industry branches. In this work, conventional WC-based powders (WC-Co and WC-Co-Cr) were sprayed with High Velocity Oxy Fuel (HVOF) onto AZ31 magnesium alloy with different spray distances (320 and 400 mm). The aim of the research was to investigate the effect of the spray distance on the microstructure of the coatings, phase composition and electrochemical corrosion resistance. Results revealed that higher spray distance results in greater porosity, 1.9% and 2.3% for 320 mm and 2.8% and 3.1% for 400 mm in case of WC-Co and WC-Co-Cr coatings, respectively. Also the influence has been observed for coatings microhardness, c.a. 1300 HV0.3 for shorter spray distance, whereas for longer one it was less than 1100 HV0.3. The corrosion resistance estimated in potentiodynamic polarization measurements was the best for WC-Co-Cr coating deposited from the shorter spray distance, corrosion current density was equal to 2.9 µA·cm-2 and polarization resistance was equal to 8424 Ω∙cm2.
Silesian University of Technology, Faculty of Mechanical Engineering, Department of Engineering Materials and Biomaterials, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Department of Metal Forming, Welding and Metrology, ul. Łukasiewicza 5, 50-371 Wroclaw, Poland
Silesian University of Technology, Faculty of Chemistry, Department of Inorganic and Analytical Chemistry and Electrochemistry, ul. Krzywoustego 6B, 44-100 Gliwice, Poland
Polish Academy of Sciences, Centre of Polymer and Carbon Materials, ul. M. Curie-Skłodowskiej 34, 41-819 Zabrze, Poland
Silesian University of Technology, Laboratory of the Testy Materials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
“RESURS” Company, A. Radziszewski, ul. Czarodzieja 12, 03-116 Warszawa, Poland
Early detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially when it comes to using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were performed on concrete beams subjected to mechanical degradation in a 3-point bending test. Ultrasonic waves were registered on a specimen surface by piezoelectric transducers located at several points. Then, the signals were processed taking advantage of wave scattering due to micro-crack disturbances. For early-stage damage detection, coda wave interferometry was used. The novelty of the work concerns the application of the complex decorrelation matrix and the moving reference trace approach for better distinguishment of sensors located in different parts of a crack zone. To enhance coda wave-based damage identification results, optical imaging of crack development was performed by means of digital image correlation measurement. The results obtained showed that the coda wave interferometry technique can be successfully used as a quantitative measure of changes in the structure of concrete. The results also indicated that the course of decorrelation coefficient curves enabled the identification of three stages during degradation, and it depended on the location of acquisition points versus the crack zone.
Department of Mechanics of Materials and Structures, Faculty of Civil and Environmental Engineering, Gdańsk University of Technology,Narutowicza 11/12, 80-233, Gdańsk, Poland
Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.
The study investigated the effect of the fill factor, lattice constant, and the shape and type of meta-atom material on the reduction of mechanical wave transmission in quasi-two-dimensional phononic structures. A finite difference algorithm in the time domain was used for the analysis, and the obtained time series were converted into the frequency domain using the discrete Fourier transform. The use of materials with large differences in acoustic impedance allowed to determine the influence of the meta-atom material on the propagation of the mechanical wave.
Faculty of Mechanical Engineering and Computer Science, Department of Mechanics and Fundamentals of Machinery Design, Czestochowa University of Technology, Dąbrowskiego 73, 42-201 Częstochowa, Poland
Faculty of Production Engineering and Materials Technology, Department of Physics, Czestochowa University of Technology, Armii Krajowej 19, 42-201 Częstochowa, Poland
Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava,70833 Ostrava, Czech Republic
Faculty of Mechanical Engineering and Computer Science, Department of Computer Science, Czestochowa University of Technology, Dąbrowskiego 73, 42-201 Częstochowa, Poland
The paper addresses an important scientific topic from the utilitarian point of view concerning the surface treatment of Al-Si-Cu aluminum alloys by PVD/ALD hybrid coating deposition. The influence of the conditions of deposition of titanium oxide in CrN/TiO2 coatings on their structure and properties, in particular corrosion resistance, were investigated. The TiO2 layer was produced by the atomic layer deposition (ALD) method with a variable number of cycles. Structural investigations were performed using scanning and transmission electron microscopy (SEM and TEM), atomic force microscopy (AFM), and Raman spectroscopy methods. Electrochemical properties were analyzed using potentiodynamic and electrochemical impedance spectroscopy (EIS) methods. The CrN/TiO2 hybrid coating with titanium oxide deposited at 500 ALD cycles showed the best corrosion properties. It was also found that the prerequisite for obtaining the best electrochemical properties was the amorphous structure of titanium oxide in the tested hybrid coatings. The high tribological properties of the tested coatings were also confirmed.
The paper concerns the optimization of a tuned mass damper with inerter (TMDI) based on two strategies, i.e., the minimum amplitude in the resonance peak and minimum area under the frequency response curve. The optimization is based on real, accessible parameters. Both optimization procedures are presented in two steps. In the first one, two parameters of the TMDI are tuned (inertance and damping coefficient), while in the second one, three parameters (mass, inertance, and damping coefficient). We show that both strategies give the optimum sets of parameters and allow the reduction of the amplitude of the damped system.
The condition monitoring of offshore wind power plants is an important topic that remains open. This monitoring aims to lower the maintenance cost of these plants. One of the main components of the wind power plant is the wind turbine foundation. This study describes a data-driven structural damage classification methodology applied in a wind turbine foundation. A vibration response was captured in the structure using an accelerometer network. After arranging the obtained data, a feature vector of 58 008 features was obtained. An ensemble approach of feature extraction methods was applied to obtain a new set of features. Principal Component Analysis (PCA) and Laplacian eigenmaps were used as dimensionality reduction methods, each one separately. The union of these new features is used to create a reduced feature matrix. The reduced feature matrix is used as input to train an Extreme Gradient Boosting (XGBoost) machine learning-based classification model. Four different damage scenarios were applied in the structure. Therefore, considering the healthy structure, there were 5 classes in total that were correctly classified. Five-fold cross validation is used to obtain a final classification accuracy. As a result, 100% of classification accuracy was obtained after applying the developed damage classification methodology in a wind-turbine offshore jacket-type foundation benchmark structure.
Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE),Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
Programa de Ingeniería Mecatrónica, Universidad de San Buenaventura, Carrera 8H #172-20, Bogota, Colombia
Laboratori de Càlcul Numèric (LaCàN), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain
With the continuous development of bridge technology, the condition assessment of large bridges has gradually attracted attention. Structural Health Monitoring (SHM) technology provides valuable information about a structure's existing health, keeping it safe and uninterrupted use under various operating conditions by mitigating risks and hazards on time. At the same time, the problem of bridge underwater structure disease is becoming more obvious, affecting the safe operation of the bridge structure. It is necessary to test the bridge’s underwater structure. This paper develops a bridge underwater structure health monitoring system by combining building information modeling (BIM) and an underwater structure damage algorithm. This paper is verified by multiple image recognition networks, and compared with the advantages of different networks, the YOLOV4 network is used as the main body to improve, and a lightweight convolutional neural network (Lite-yolov4) is built. At the same time, the accuracy of disease identification and the performance of each network are tested in various experimental environments, and the reliability of the underwater structure detection link is verified.
The structural, morphological and photoluminescent properties of thermally evaporated neodymium oxide (Nd2O3) thin films deposited onto nanostructured silicon (Si-ns) are reported. Si-ns embedded in silicon nitride (SiN) thin films are prepared by plasma-enhanced chemical vapour deposition (PECVD). SiN and Nd2O3 thin films uniformity and Si-ns formation are confirmed by atomic force microscopy (AFM) and scanning electron microscopy (SEM). The presence of neodymium (Nd), silicon (Si), oxygen (O), and phosphorus (P) is investigated by energy-dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS). Post-annealing SIMS profile indicates an improvement of the homogeneity of activated P distribution in Si bulk. The X-ray diffraction (XRD) combined with Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR) have been employed to determine amorphous silicon (a-Si), crystalline silicon (c-Si), Nd2O3 and SiN phases present in the Nd2O3-SiN bilayers with their corresponding chemical bonds. After annealing, a Raman shift toward lower wavenumbers is recorded for the Si peak. XPS data reveal the formation of Nd2O3 thin films with Nd-O bonding incorporating trivalent Nd ions (Nd3+). Strong room-temperature photoluminescence is recorded in the visible light range from the Si-ns. Nd-related photoluminescent emission in the near infrared (NIR) range is observed at wavelengths of 1025–1031 nm and 1083 nm, and hence is expected to improve light harvesting of Si-based photovoltaic devices.
Centre de Recherche en Technologie des Semi-conducteurs pour l’Énergétique (CRTSE), 02 Bd Frantz Fanon BP140, Alger–7 merveilles, 16027 Algiers, Algeria
Faculté de Physique, Université des Sciences et de la Technologie Houari Boumediene (USTHB), BP 32 Bab-Ezzouar, 16111 Algiers, Algeria
Centre de Développement des Technologies Avancées (CDTA), Cité 20 août 1956, 16081 Algiers, Algeria
Université Abou Bekr Belkaid BP 230, 13000 Chetouane, Tlemcen, Algeria
In the last decade, infrared imaging detectors trend has gone for smaller pixels and larger formats. Most of the time, this scaling is carried out at a given total sensitive area for a single focal plane array. As an example, QVGA 30 µm pitch and VGA 15 µm pitch exhibit exactly the same sensitive area. SXGA 10 µm pitch tends to be very similar, as well. This increase in format is beneficial to image resolution. However, this scaling to even smaller pixels raises questions because the pixel size becomes similar to the IR wavelength, but also to the typical transport dimensions in the absorbing material. Hence, maintaining resolution for such small pixel pitches requires a good control of the modulation transfer function and quantum efficiency of the array, while reducing the pixel size. This might not be obtained just by scaling the pixel dimensions. As an example, bulk planar structures suffer from excessive lateral diffusion length inducing pixel-to-pixel cross talk and thus degrading the modulation transfer function. Transport anisotropy in some type II superlattice structures might also be an issue for the diffusion modulation transfer function. On the other side, mesa structures might minimize cross talk by physically separating pixels, but also tend to degrade the quantum efficiency due to a non-negligible pixel fill factor shrinking down the pixel size. This paper discusses those issues, taking into account different material systems and structures, in the perspective of the expected future pixel pitch infrared focal plane arrays.
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