Search Results (51)

In response to the problem of complex and variable loads on wind turbine gearbox bearing in working conditions, as well as the limited amount of sound data making fault identification difficult, this study focuses on sound signals and proposes an intelligent diagnostic method using deep learning. By adding the CBAM module in ResNeXt to enhance the model’s attention to important features and combining it with the Arcloss loss function to make the model learn more discriminative features, the generalization ability of the model is strengthened. We used a fine-tuning transfer learning strategy, transferring pre-trained model parameters to the CBAM-ResNeXt50-ArcLoss model and training with an extracted Mel spectrogram of sound signals to extract and classify audio features of the wind turbine gearbox. Experimental validation of the proposed method on collected sound signals showed its effectiveness and superiority. Compared to CNN, ResNet50, ResNeXt50, and CBAM-ResNet50 methods, the CBAM-ResNeXt50-ArcLoss model achieved improvements of 13.3, 3.6, 2.4, and 1.3, respectively. Through comparison with classical algorithms, we demonstrated that the research method proposed in this study exhibits better diagnostic capability in classifying wind turbine gearbox sound signals. Full article

Electric arc furnaces are commonly used in foamed slag technology for the production of steel from steel scrap through an electric process. The effects of using this technology include increased efficiency, reduced consumption of refractory materials, reduced energy consumption, reduced electrode wear, and improved arc stability. The world is constantly looking for solutions to optimize the feeding of the foaming agent to the electric furnace, including determining the moment of starting its feeding. The authors propose using two parameters to determine the optimal moment of introducing the foaming agent: the change in the sound level emitted by the arc furnace and the fluctuations in active power consumption. In order to determine the above parameters, tests were carried out on an industrial alternating UHP arc furnace with a capacity of 70 tons. The sound intensity level was determined at which the feeding of the foaming agent to the furnace’s working space should begin. A moving coefficient of power consumption variation was developed and decision variables of the software for online foaming agent feeding were determined. As a result of implementing the developed solutions to the electric furnace control system and conducting comparative tests, savings were obtained in the form of reduced foaming agent consumption. Full article

High frequency and large bandwidth are growing trends in communication radio-frequency devices. The LiNbO₃ thin film material is expected to become the preferred piezoelectric material for high coupling resonators in the 5G frequency band due to its ultra-high piezoelectric coefficient and low loss characteristics. The main mode of laterally excited bulk acoustic wave resonators (XBAR) have an ultra-high sound velocity, which enables high-frequency applications. However, the interference of spurious modes is one of the main reasons hindering the widespread application of XBAR. In this paper, a Z-cut LiNbO₃ thin film-based XBAR with arc-shaped electrodes is presented. We investigate the electric field distribution of the XBAR, while the irregular boundary of the arc-shaped electrodes affects the electric field between the existing interdigital transducers (IDTs). The mode shapes and impedance response of the XBAR with arc-shaped electrodes and the XBARs with traditional IDTs are compared in this work. The fabricated XBAR on a 350 nm Z-cut LiNbO₃ thin film with arc-shaped electrodes operating at over 5 GHz achieves a high effective electromechanical coupling coefficient of 29.8% and the spurious modes are well suppressed. This work promotes an XBAR with an optimized electrode design to further achieve the desired performance. Full article

Ecosystem services serve as a bridge between the ecological environment and human society. The quantitative analysis and forecasting of ecosystem services can provide references for regional eco-environmental assessments and land-use planning for the future. In this study, taking Hainan Tropical Rainforest National Park (HTRNP) as an example, the value of regulating ecosystem services (RESs) in 2020 was assessed via ArcGIS 10.1 and the InVEST 3.5 model, and the per-unit value of RESs was calculated for different LULC types. In addition, in accordance with the Overall Planning for HTRNP and the objective of optimizing RESs, the value of RESs in short-term (to 2030) and long-term (to 2050) scenarios was forecast via a linear programming model. The results are as follows: (1) The RES value of HTRNP in 2020 was CNY 2090.67 × 10⁸, with climate regulation accounting for the largest proportion; the spatial distribution of RESs in the eastern and central areas was higher than that in the western area, but different indicators of RESs differed in their spatial patterns in varied geographic units. (2) The natural forest ecosystem in HTRNP accounts for 76.94% of the total area but 84.82% of the total value of RESs. The per-unit value is ranked from highest to lowest as follows: montane rainforests > wetlands > lowland rainforests > lowland secondary rainforests > tropical coniferous forests > deciduous monsoon rainforests > tropical cloud forests > shrub forests > timber forests > economic forests > rubber forests > grasslands > farmlands > settlements. (3) In the short-term scenario, the value of RESs is CNY 2216.64 × 10⁸, an increase of CNY 118.97 × 10⁸ compared to 2020, with an increase rate of 5.67%. In the long-term scenario, the value of RESs is CNY 2472.48 × 10⁸, an increase of CNY 374.81 × 10⁸ compared to 2020, with an increase rate of 17.87%. The results reveal the significance of ecosystem services in the national park and can inform more targeted and scientifically sound decision-making in the future. Full article

Objectives. This study aimed to compare the audiological benefits of a non-implantable wearable option for a bone conduction (BC) implant mounted on an arch (SoundArc) to those of traditional BC hearing aids (HAs) mounted on eyeglasses (BCHAs) in patients with moderate to severe conductive or mixed hearing loss. Methods: A preliminary cross-sectional observational prospective cohort study was conducted in the Tertiary Audiological Department, University Hospital. Fourteen adults with conductive or mixed hearing loss (PTA at 0.5-1-2-4 KHz = 67 ± 15 dB HL) who had been wearing conventional BCHAs mounted on eyeglasses for at least 3 years and had declined surgical implantation of a bone conduction hearing implant (BCHI) were included in the study. Unaided and aided pure-tone air conduction (AC) and bone conduction (BC) thresholds, as well as speech tests in quiet and noise, were recorded at baseline and in two different settings: with a BCHI mounted on SoundArc^® and with their own BCHAs mounted on eyeglasses using two couplers. Participants completed questionnaires in both conditions, including the International Inventory for Hearing Aids (IOI-HA), the Hearing Handicap Inventory for Adults/Elderly (HHIA/E), the Speech, Spatial, and Qualities of Hearing Scale (SSQ), a 10-point visual analog scale (VAS), and the Fatigue Impact Scale (FIS). Results: A significant functional gain was observed in both settings (p = 0.0001). Better speech perception in quiet and noise was observed with SoundArc compared to conventional BCHAs on eyeglasses (improvements in word repetition scores in noise: +19.3 at SNR +10 dB, p = 0.002; +12.1 at SNR 0 dB, p = 0.006; and +11.4 at SNR −10 dB, p = 0.002). No significant differences were found in IOI-HA, FIS, and HHIA/E scores. However, significantly better SSQ scores were reported for SoundArc in all domains (p = 0.0038). Conclusions: Although patients were accustomed to using BCHAs mounted on eyeglasses, the bone conduction wearable option of the BCHI (SoundArc) proved to be a viable alternative for adult patients with conductive or mixed hearing loss who are unable or unwilling to undergo BCHI surgery. Full article

Flow ultrasound generators are devices that emit high-frequency sound waves due to the hydrodynamic instability of the supersonic flow. In the electric power industry, such generators are used in arc quenching systems of high-voltage gas blast circuit breakers. The design of the flow ultrasound generator includes a nozzle and a hollow cylindrical resonator. Self-oscillations of the sealing waves occur when a supersonic gas jet collides with a resonator. This article is devoted to the analysis of the arrester operation, which has design features of flow ultrasound generators. The paper includes both experimental investigations of the proposed spark gap arrester design and mathematical modeling of the processes occurring in it. A description of the methods used is presented, and a comparison of the time dependences of currents and voltages obtained as a result of experimental studies and as a result of calculations is performed in the next section. The calculation results include oscillograms of the voltage and current, and the plasma temperature distribution in the arrester chamber at different moments of time. The investigations show that the presence of a nozzle and resonator leads to an intensification of the gas dynamic effect on the electric arc. Full article

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier. Full article

With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project “3DPrintFEM”, a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm’s detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the algorithm will be integrated into an equipment developed in-house to monitor the quality of samples produced, thereby having an in-house quality assessment routine. The equipment shall be less expensive than conventional acoustic equipment, thus helping the industry cut costs when validating the quality of their components. Full article

Single-crystal diamond tools occupy an important position in the field of optical processing as the basis and key to advanced optical manufacturing technology, such as grating manufacturing and optical mirror-turning processing. Single-crystal diamond tools have become the cornerstone of the development of related industries. This paper takes a single-crystal diamond arc tool as the research object. Sound signal analysis technology and vibration signal analysis technology are comprehensively applied to the online orientation identification of a single-crystal diamond tool in the indexing grinding process. The online orientation method of the tool is explored, the sound signal and the vibration signal are taken as the characteristic signals, and a wavelet algorithm (WT) is used to reduce the noise of the vibration signal and sound signal. The kurtosis of the sound signal and the kurtosis and skewness of the vibration signal in the high-order statistics strongly related to the grinding direction of a single-crystal diamond are used as the characteristic parameters, and the online direction recognition model of the tool is established using the Hidden Markov Method (HMM). The above characteristic parameters are used as model input for multi-information fusion. The mapping relationship between the characteristic parameters of the characteristic signal and the crystal orientation of the single-crystal diamond crystal face is obtained, and then the online orientation method of the single-crystal diamond arc tool in the process of indexing grinding is formed. The effectiveness of the method is verified by experiments, and effective orientation information is provided for research on the positioning control strategy of the tool grinding process to ensure the efficiency of grinding and improve the manufacturing level of the tool. Full article

The present study encompasses a thorough analysis of the vibrations in a splash musical cymbal. The analysis is performed using a hybrid methodology that combines experimental measurements with parametric computer-aided design and finite element method simulations. Experimental measurements, including electronic speckle pattern interferometry, and impulse response measurements are conducted. The interferometric measurements are used as a reference for the evaluation of finite element method modal analysis results. The modal damping ratio is calculated via the impulse response measurements and is adopted by the corresponding simulations. Two different approximations are employed for the computer-aided design and finite element method models: one using three-point arcs and the other using lines to describe the non-smooth curvature introduced during manufacturing finishing procedures. The numerical models employing the latter approximation exhibit better agreement with experimental results. The numerical results demonstrate that the cymbal geometrical characteristics, such as the non-smooth curvature and thickness, greatly affect the vibrational behavior of the percussion instrument. These results are of valuable importance for the development of vibroacoustic numerical models that will accurately simulate the sound synthesis of cymbals. Full article

Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement in technology, intelligent welding technology is expected to become a solution to this problem in the future. To achieve the intelligent welding process, modern sensing technology can be employed to effectively simulate the welder’s sensory perception and cognitive abilities. Recent studies have advanced the application of sensing technologies, leading to the advancement in intelligent welding process. The review is divided into two aspects. First, the theory and applications of various sensing technologies (visual, sound, arc, spectral signal, etc.) are summarized. Then, combined with the generalization of neural networks and attention mechanisms, the development trends in welding sensing information processing and modeling technology are discussed. Based on the existing research results, the feasibility, advantages, and development direction of attention mechanisms in the welding field are analyzed. In the end, a brief conclusion and remarks are presented. Full article

This study aims at investigating the feasibility of depositing quality coatings from various high-entropy intermetallic compounds (HEICs) using detonation spraying (DS). Four different HEIC coatings, namely (NbTaVCrTi)Al₃, (NbTaVNiFe)Al₃, (NbTaVZrHf)Al₃, and (FeNiCoCrMn)(MoCr), were prepared by DS on low alloy steel substrates. The HEIC powders were first prepared by arc melting followed by ball milling and then used as reinforcement particles to deposit HEIC coatings. Elemental segregation was observed for all the as-cast samples. Powders with average particle sizes of about ~25 µm for (NbTaVCrTi)Al₃, ~22 µm for (NbTaVNiFe)Al₃, ~34 µm for (NbTaVZrHf)Al₃, and ~18 µm for (FeNiCoCrMn)(MoCr) were obtained. (NbTaVCrTi)Al₃, (NbTaVNiFe)Al₃, and (NbTaVZrHf)Al₃ HEICs exhibited a nearly single D0₂₂ (TaAl₃ type) structure, while (FeNiCoCrMn)(MoCr) exhibited a single D8_b (FeCr type) structure. Dense coatings consisted of a lamellar microstructure and sound bonding with the substrate, and low porosity was obtained for all the samples. Crystal structures of the HEIC samples were highly retained during DS, whereas all the samples underwent some degree of oxidation. Microhardness values of 745 HV for (NbTaVCrTi)Al₃, 753 HV for (NbTaVNiFe)Al₃, and 862 HV for (NbTaVZrHf)Al₃ were obtained, which are significantly higher than the microhardness of the substrate (~140 HV). Among all the samples, (FeNiCoCrMn)(MoCr) exhibited the highest microhardness values of about 1047 HV. Full article

The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet. Full article

In the current framework of energy transition, renewable energy production has gained a renewed relevance. A set of 75 papers was selected from the existing literature and critically analyzed to understand the main inputs and tools used to calculate solar energy and derive theoretical photovoltaic production based on geographic information systems (GISs). A heterogeneous scenario for solar energy estimation emerged from the analysis, with a prevalence of 2.5D tools—mainly ArcGIS and QGIS—whose calculation is refined chiefly by inputting weather data from databases. On the other hand, despite some minor changes, the formula for calculating the photovoltaic potential is widely acknowledged and includes solar energy, exploitable surface, performance ratio, and panel efficiency. While sectorial studies—targeting a specific component of the calculation—are sound, the comprehensive ones are generally problematic due to excessive simplification of some parts. Moreover, validation is often lacking or, when present, only partial. The research on the topic is in constant evolution, increasingly moving towards purely 3D models and refining the estimation to include the time component—both in terms of life cycle and variations between days and seasons. Full article

Ni-based composite coatings reinforced by high-entropy intermetallic compounds (HEICs) were prepared by detonation spraying (DS) on low alloy steel substrates. To this end, first (Ti-Nb)(V-Cr-Ni-Fe) and Al₃(TiZrNbCrHfTa) HEIC powders were fabricated by arc melting followed by ball milling. The as-milled HEIC powders were then employed as reinforcement particles to prepare Ni-7wt.% HEIC composite coatings. The average particle size of the (Ti-Nb)(V-Cr-Ni-Fe) and Al₃(TiZrNbCrHfTa) HEIC powders were 18 and 35 µm, respectively, while the average particle size of the Ni powder was 56 µm. (Ti-Nb)(V-Cr-Ni-Fe) exhibited a single hexagonal C14 Laves phase in spite of Ti and Nb segregations. The XRD pattern of Al₃(TiZrNbCrHfTa) indicated the presence of a tetragonal D0₂₂-type structure along with some minor CrTi and Cr₅Al₈ phases. The sprayed Ni-7wt.% FeNiCrV-TiNb and Ni-7wt.% Al₃(TiZrNbCrHfTa) composite coatings retained crystal structures of the powder mixtures, suggesting proper thermal stability for both powders. The coatings exhibited a dense microstructure consisting of a lamellar microstructure with low porosity and sound bonding with the substrate. The microhardness of Ni-7wt.% FeNiCrV-TiNb (450 HV) was higher than that of Al₃(TiZrNbCrHfTa) (338 HV), and it exhibited lower fluctuation than that of Ni-7wt.% Al₃(TiZrNbCrHfTa). DS is an effective method to fabricate metal matrix composites reinforced by HEICs with a low level of porosity. Full article