Frequency Characteristics Research Articles

Experimental investigation on aerodynamic noise of a small-scale multi-blade centrifugal fan

In this study, the aerodynamic noise of a small-scale centrifugal fan was experimentally investigated using acoustic testing techniques and time-resolved stereoscopic particle image velocimetry (SPIV). During the acoustic experiments, both far-field noise and near-field pressure fluctuations of the test fan were measured. The overall far-field noise towards the fan inlet side was found to be higher than that of the back side. The pressure fluctuations on the fan upper casing exceeded those on the side wall due to the uncontracted volute tongue, indicating pronounced flow-to-wall interactions. Moreover, based on a simultaneous measurement, the coherence between the near-field pressure fluctuations and far-field noise highlighted the significant contributions of impeller rotation to noise radiation. SPIV measurements uncovered the time-averaged and transient flow fields at the fan's inlet and outlet. The time-averaged results demonstrated the concentrated inlet flow and outlet flow separation, leading to high flow unsteadiness. Transient flow fields displayed an asymmetric jet-wake region characterized by both quasi-steady flow and rotational flow behaviours. The instantaneous flow results were analyzed using the dynamic mode decomposition (DMD) method, which clearly recognized the jet-wake patterns with frequencies corresponding to the rotational frequency. The observed consistency in frequency characteristics among noise, pressure fluctuations, and unsteady flow affirms that flow dynamics are crucial to the primary noise mechanisms.

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance compared to frequency-domain inversion. Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution. Therefore, the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution, stability, and noise resistance. The introduction of prior information constraints can effectively reduce ambiguity in the inversion process. However, the existing model-driven time-frequency joint inversion assumes a specific prior distribution of the reservoir. These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features. Therefore, this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain. The method is based on the impedance and reflectivity samples from logging, using joint dictionary learning to obtain adaptive feature information of the reservoir, and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity. The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation. We have finally achieved an inversion method that combines constraints on time-domain features and frequency features. By testing the model data and field data, the method has higher resolution in the inversion results and good noise resistance.

Dynamics and mitigation strategies of knock in hydrogen-fueled internal combustion engines

In the context of utilizing hydrogen as a fuel for internal combustion engines, it is crucial to address its tendency to induce knock more readily under certain conditions compared to gasoline. Knock not only reduces fuel efficiency but also has the potential to inflict detrimental effects on engine components. It is generally believed that knock is primarily caused by the spontaneous combustion of the end-gas mixture. Current research focuses on the frequency, intensity, and statistical characteristics of knock signals.This study aims to introduce two prevalent and effective methods for mitigating knock: increasing the Exhaust Gas Recirculation (EGR) ratio and water injection into the engine. Experimental observations suggest that at low rotational speeds (n=900 rpm), the amplitude spectra of knock induced by hydrogen and gasoline show similarities. However, at higher speeds (n>3000 rpm), the amplitude of hydrogen-induced knock not only exhibits a greater number of peak values but also intensifies in strength. Notably, the efficacy of increasing the EGR ratio diminishes at high speeds, potentially due to the propensity of methane to also cause knock under high-temperature conditions. In contrast, water injection, while generally effective, may adversely impact the lifespan of engine components.In summary, effective suppression of knock in hydrogen-fueled internal combustion engines is vital for accelerating the adoption of hydrogen in the power sector and making significant contributions to environmental conservation. This paper aims to delve into the analysis of these methods effectiveness and proposes directions for future research.

Observation of a phase transition from a continuous to a discrete time crystal

Discrete (DTCs) and continuous time crystals (CTCs) are novel dynamical many-body states, that are characterized by robust self-sustained oscillations, emerging via spontaneous breaking of discrete or continuous time translation symmetry. DTCs are periodically driven systems that oscillate with a subharmonic of the external drive, while CTCs are continuously driven and oscillate with a frequency intrinsic to the system. Here, we explore a phase transition from a continuous time crystal to a discrete time crystal. A CTC with a characteristic oscillation frequency ωCTC is prepared in a continuously pumped atom-cavity system. Modulating the pump intensity of the CTC with a frequency ωdr close to 2ωCTC leads to robust locking of ωCTC to ωdr/2 , and hence a DTC arises. This phase transition in a quantum many-body system is related to subharmonic injection locking of non-linear mechanical and electronic oscillators or lasers.

Relation between fault characteristic frequencies and local interpretability shapley additive explanations for continuous machine health monitoring

Recently, the Shapley additive explanations models have been extensively studied to enhance explainability of artificial intelligence algorithms, while most of them simply use Shapley additive explanations to rank or measure the importance of different features. In this study, a novel methodology that studies the relation between fault characteristic frequencies and Shapley values generated by local interpretability Shapley additive explanations for machine health monitoring is proposed. Firstly, a simulation model is introduced to generate vibration signals at different health conditions and their spectral amplitudes transformed from Fourier transform are used to investigate the relationship between fault characteristic frequencies and local interpretability Shapley values. It is interestingly found that Shapley values can be used to locate fault characteristic frequencies. Moreover, most of them have negative values in a normal stage and have positive values in an abnormal stage. Based on this finding and Shapley additive explanations, a health indicator construction methodology is proposed to continuously monitor incipient machine faults. Subsequently, an automatic signal filtering method is proposed to remove and eliminate burrs and noise in Shapley values so that fault characteristic frequencies can be clearly revealed by Shapley values for physical fault diagnosis. Two run-to-failure cases are conducted to demonstrate the effectiveness of the proposed methodology and then the superiority of this study is demonstrated by comparing with existing methods for health indicator construction and fault diagnosis, including sparsity parameters, Hjorth parameters, and fast Kurtogram. Comparison results show that the proposed health indicator is more sensitive to the time of incipient fault initiation and interpretable fault diagnosis based on Shapley values has a robust performance. This study first sheds a light on the relationship between fault characteristic frequencies and Shapley values under the scenario of continuous machine health monitoring and seamlessly guides applicants to realize Shapley additive explanations based incipient fault detection and diagnosis.

Acoustic black hole ultrasonic scalpel

Ultrasonic scalpels (USs), as the preferred energy instruments, are facing a growing need to exhibit enhanced performance with the diversification of modern surgical challenges. Hence, we proposed an acoustic black hole ultrasonic scalpel (ABHUS) in longitudinal-bending coupled vibration for efficient surgical cutting. By incorporating an acoustic black hole profile, the local bending wave velocity is reduced and the amplitude is amplified cumulatively, thus creating a high-energy region near the blade tip to enhance the cutting performance of the ABHUS. The precise physical analysis model is established for systematic design of the ABHUS and quick estimation of its frequency characteristics. The vibration simulation and experiments demonstrate that compared with the conventional ultrasonic scalpel (CUS), the output amplitude of the ABHUS significantly increases, particularly a 425% increase in bending vibration displacement. The in-vitro cutting experiment confirms that ABHUS exhibits superior cutting performance. Our design presents vast possibilities and potential for the development of high-performance ultrasonic surgical instruments, serving as an innovative supplement with extraordinary significance for application of acoustic black holes.

High-frequency vibration of beveled crystal plates by using subregional geometric fitting method

The beveling process is an important process in the manufacturing of resonators, which has a significant impact on the frequency stability of resonators. Without understanding the frequency characteristics of the resonator after beveling, it is impossible to accurately design the beveled resonators. Thus, in order to investigate the vibration characteristics of AT-cut beveled resonators, we investigated the high-frequency vibration in this work by using the subregional geometric fitting method (SGFM) based on Mindlin’s plate theory. Quartz crystal plates with nonuniform thicknesses are partitioned into three regions and each region is fitted by using the polynomial functions based on the measured geometric morphology data. The governing equations are obtained based on Mindlin’s two-dimensional theory and the coupled vibrations are further solved using the partial differential equation module of COMSOL. In the numerical calculation, we compare the results obtained by the SGFM with those obtained by the global fitting method and the measured data. The accuracy and effectiveness of the SGFM are also verified. It is found that the frequencies obtained by the SGFM are slightly higher than the frequencies obtained by the global fitting method, and the results of SGFM are closer to the measured results. Meanwhile, as the beveling time increases, the frequency increases and the energy trapping effect becomes more significant. The proposed method can significantly improve the computational efficiency of thickness-shear vibration while ensuring accuracy. It is expected to provide a new geometric fitting method for the analysis of beveled crystal resonators.

Hemodynamic effects of pulsatile frequency of right ventricular assist device (RVAD) on pulmonary perfusion: a simulation study.

Right ventricular assist devices (RVADs) have been extensively used to provide hemodynamic support for patients with end-stage right heart (RV) failure. However, conventional in-parallel RVADs can lead to an elevation of pulmonary artery (PA) pressure, consequently increasing the right ventricular (RV) afterload, which is unfavorable for the relaxation of cardiac muscles and reduction of valve complications. The aim of this study is to investigate the hemodynamic effects of the pulsatile frequency of the RVAD on pulmonary artery. Firstly, a mathematical model incorporating heart, systemic circulation, pulmonary circulation, and RVAD is developed to simulate the cardiovascular system. Subsequently, the frequency characteristics of the pulmonary circulation system are analyzed, and the calculated results demonstrate that the pulsatile frequency of the RVAD has a substantive impact on the pulmonary artery pressure. Finally, to verify the analysis results, the hemodynamic effects of the pulsatile frequency of the RVAD on pulmonary artery are compared under diffident support modes. It is found that the pulmonary artery pressure decreases by approximately 6% when the pulsatile frequency changes from 1 to 3Hz. The increased pulsatile frequency of RA-PA support mode may facilitate the opening of the pulmonary valve, while the RV-PA support mode can more effectively reduce the load of RV. This work provides a useful method to decrease the pulmonary artery pressure during the RVAD supports and may be beneficial for improving myocardial function in patients with end-stage right heart failure, especially those with pulmonary hypertension.

Lock-in spectrum: a tool for representing long-term evolution of bearing fault in the time–frequency domain using vibration signal

PurposeThis study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.Design/methodology/approachThe Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.FindingsExperimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.Originality/valueThe proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

Large Local Internal Stress in an Elastically Bent Molecular Crystal Revealed by Raman Shifts.

The structural dynamics involved in the mechanical flexibility of molecular crystals and the internal stress in such flexible materials remain obscure. Here, the study reports an elastically bending lipidated molecular crystal that shows systematic shifts in characteristic vibrational frequencies across the bent crystal region - revealing the nature of structural changes during bending and the local internal stress distribution. The blueshifts in the bond stretching modes (such as C═O and C-H modes) in the inner arc region and redshifts in the outer arc region of the bent crystals observed via micro-Raman mapping are counterintuitive to the bending models based on intermolecular hydrogen bonds. Correlating these shifts with the trends observed from high-pressure Raman studies on the crystal reveals the local stress difference between the inner arc and outer arc regions of the bent crystal to be ≈2GPa, more than an order of magnitude higher than the previously proposed value in elastically bending crystals. High local internal stress can have direct ramifications on the properties of molecular piezoelectric energy harvesters, actuators, semiconductors, and flexible optoelectronic materials.

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.

Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

Assessing the visual landscape of Istanbul Bosphorus: Exploring the role of vegetation and built environment characteristics

Istanbul Bosphorus is one of the most important protected landscapes of Türkiye with its dramatic topography, natural landscape, and historical and cultural heritage embedded within this natural setting. However, studies focusing on the visual landscape assessment (VLA) of Bosphorus are limited. The main goal was to evaluate the VLA of Bosphorus and to understand the users' perception of this multi-layered landscape via utilizing VLA based on the evaluation of 45 photographs taken from the sea. For this goal, the study investigates the environmental texture proportion (ETP: vegetation, built environment, sea, and sky) and RGB values of the photographs using parameters such as naturalness, beautiful scenery, disturbance, excitement, safety, and livability in 5-Likert’s scale. Findings demonstrated that photographs with more vegetation than built environment, with those containing 10 % or more vegetation receiving higher overall scores. Vegetation showed a high positive correlation with naturalness, beautiful scenery, excitement, safety, and livability. Moreover, disturbance was positively correlated with the built environment, and negatively correlated with vegetation. Namely, the most problematic ETP type for VLA was the built environment in Bosphorus. The study further identified built environment characteristics as a critical environmental component, with historical and mansion-type buildings scoring higher than other building types. Another important finding was that certain user features, such as gender, influenced perceptions of beautiful scenery (9 p-value), excitement (10), safety (11), and livability (21), while usage frequency affected perceptions of excitement (14) and safety (13). In other words, men gave higher scores to photographs compared to women, and users who regularly utilized the study area rated the photographs more favorably than those who used it less frequently. In conclusion, the study found that user characteristics of gender and usage frequency were influential factors, and vegetation (10 % and above) was the most paramount element for preferences when considering the visual landscape of the Bosphorus in Istanbul.

Frequency and clinicopathologic features of renal low-grade oncocytic tumour and eosinophilic vacuolated tumour: reclassification of 605 eosinophilic tumours including patients managed with active surveillance

AimsLow-grade oncocytic tumour (LOT) and eosinophilic vacuolated tumour (EVT) are recently described emerging entities, which demonstrate distinct features but are not yet recognised as separate neoplasms in the fifth WHO...

Infrared Image Super-Resolution Network Utilizing the Enhanced Transformer and U-Net.

Infrared images hold significant value in applications such as remote sensing and fire safety. However, infrared detectors often face the problem of high hardware costs, which limits their widespread use. Advancements in deep learning have spurred innovative approaches to image super-resolution (SR), but comparatively few efforts have been dedicated to the exploration of infrared images. To address this, we design the Residual Swin Transformer and Average Pooling Block (RSTAB) and propose the SwinAIR, which can effectively extract and fuse the diverse frequency features in infrared images and achieve superior SR reconstruction performance. By further integrating SwinAIR with U-Net, we propose the SwinAIR-GAN for real infrared image SR reconstruction. SwinAIR-GAN extends the degradation space to better simulate the degradation process of real infrared images. Additionally, it incorporates spectral normalization, dropout, and artifact discrimination loss to reduce the potential image artifacts. Qualitative and quantitative evaluations on various datasets confirm the effectiveness of our proposed method in reconstructing realistic textures and details of infrared images.

Split-type seismic metamaterials with pseudo-surface wave weakening mechanism

Pseudo surface waves, as a type of propagating wave, are detrimental to the attenuation of seismic Rayleigh waves. A split seismic metamaterial (SSM) that can weaken pseudo surface wave modes and generate ultra wideband gaps of 12.92Hz is proposed. The split configuration of SSM can be obtained by separating the modes of different waves within the energy band curve, which can cause the surface waves inside the sound cone to move outward and weaken the pseudo surface wave modes inside the band gap. Combined with the frequency response curve, it is further demonstrated that the attenuation amplitude of SSM is larger than that of monolithic seismic metamaterials (MSM) within the same bandgap range. Afterwards, by studying the changes in bandgap through structural parameters, the method of a gradient array was used to fuse the characteristic frequency that can generate larger attenuation with the weakened pseudo surface wave mode frequency, resulting in greater attenuation. Finally, it is verified by experiments that SSM can provide ultra-wide low-frequency band gaps while also generating large attenuation accuracy, which provides a practical solution for the application of seismic metamaterials.

Exploring the AC conductivity and dielectric properties of PVA films doped with Erythrosin B

Erythrosin B (EB) doped in PVA film was processed utilizing the casting procedure, and the structure was investigated via the Fourier Transformation infrared technique (FTIR). FTIR analysis presented a strong formation of intermolecular hydrogen bonds among PVA and EB hydroxyl groups (OH). Characteristic temperature and frequency variation of dielectric behavior and AC electrical conductivity for films of PVA doped with Erythrosin B were analyzed at (293–383 K) temperature and (102–106 Hz) frequency. The AC conductivity dependency indicates that the predominant conduction mechanism in PVA doped with Erythrosin B films follows the correlated barrier hopping (CBH) model. The charge carriers’ hopping involving localized states causes a relaxation process. Estimates were made for the AC conductivity activation energy and the conductivity relaxation energy. Frequency and temperature affect the dielectric constant (ε′) and the dielectric loss (ε″). The complex impedance spectrum (Z′ versus Z″) showed single semicircular arcs with a decreased radius with increasing temperature. The relaxation peaks in the electric modulus representation are clearly defined, and the relaxation period has been calculated.

U-TFF: A U-Net-Based Anomaly Detection Framework for Robotic Manipulator Energy Consumption Auditing Using Fast Fourier Transform

Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.