Search Results (5,185)

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD). Full article

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action. Full article

This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor λ. The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%. Full article

Gear transmission system fault diagnosis is crucial for the reliability and safety of industrial machinery. The combination of mathematical signal processing methods with deep learning technology has become a research hotspot in fault diagnosis. Firstly, the development and status of gear transmission system fault diagnosis are outlined in detail. Secondly, the relevant research results on gear transmission system fault diagnosis are summarized from the perspectives of time-domain, frequency domain, and time-frequency-domain analysis. Thirdly, the relevant research progress in shallow learning and deep learning in the field of fault diagnosis is explained. Finally, future research directions for gear transmission system fault diagnosis are summarized and anticipated in terms of the sparsity of signal analysis results, separation of adjacent feature components, extraction of weak signals, identification of composite faults, multi-factor combinations in fault diagnosis, and multi-source data fusion technology. Full article

In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability. Full article

Proportion Integration Differentiation (PID) is a common clock disciplining algorithm. In satellite clock source equipment and in Internet of Things (IoT) sensor nodes it is usually required that both time and frequency signals have high accuracy. Because the traditional PID clock disciplining method used in the equipment only performs PID calculation and feedback control on single variable, such as frequency, the time accuracy error of the clock source is large and even has inherent deviation. By using the integral relationship between frequency and time, a new two variables PID control algorithm for high-precision clock disciplining is proposed in this paper. Time is taken as the constraint variable to make the time deviation converge. It can guarantee a high accuracy of time and high long-term stability of frequency. At the same time, frequency is taken as the feedback variable to make frequency obtain fast convergence. It can ensure high short-term stability of the frequency and the continuity of time. So, it can make the time and frequency of the disciplined clock have high accuracy and stability at the same time. In order to verify the effectiveness of the proposed algorithm, it is simulated based on the GNSS disciplined clock model. The GNSS time after Kalman filtering is used as the time reference to discipline the local clock. The simulation results show that the time deviation range of a local clock after convergence is −0.38 ns∼0.31 ns, the frequency accuracy is better than $1\times {10}^{-15}$ averaging over one day, and the long-term time stability (TDEV) for a day is about 7 ps when using the two variables PID algorithm. Compared with the single variable PID algorithm, the time accuracy of the two variables PID algorithm is improved by about one order of magnitude and the long-term time stability (TDEV) is improved by about two orders of magnitude. The research results indicate that the two variables PID control algorithm has great application potential for the development of clock source equipment and other bivariate disciplining scenarios. Full article

Sea level monitoring is of great significance to the life safety and daily production activities of coastal residents. In recent years, GNSS interferometric reflectometry (GNSS-IR) has gradually developed into a powerful complementary technique for sea level monitoring, with the advantages of wide signal spatial coverage and lower maintenance cost. However, GNSS-IR-retrieved sea level estimates suffer from a prominent error source, referred to as the dynamic height error due to the nonstationary sea level. In this study, the tidal analysis method, least squares method and cubic spline fitting method are used to correct the dynamic height error, and their performances are analyzed. These three methods are applied to multi-system and multi-frequency data from three coastal GNSS stations, MAYG, SC02 and TPW2, for three years, and the retrievals are compared and analyzed with the in situ measurements from co-located tide gauges to explore the applicability of the three methods. The results show that the three correction methods can effectively correct the sea level dynamic height error and improve the accuracy and reliability of the GNSS-IR sea level retrievals. The tidal analysis method shows the best correction performance, with an average reduction of 39.3% (10.7 cm) and 37.6% (6.7 cm) in RMSE at the MAYG and TPW2 stations, respectively. At station SC02, the cubic spline fitting method performs the best, with the RMSE reduced by an average of 39.3% (5.5 cm) after correction. Furthermore, the iterative process of the tidal analysis method is analyzed for the first time. We found the tidal analysis method could significantly remove the outliers and correct the dynamic height error through iterations, generally superior to the other two correction methods. With the dense preliminary GNSS-IR sea level retrievals, the smaller window length of the least squares method can yield more corrected retrievals and better correction performance. The least squares method and cubic spline fitting method, especially the former, are highly dependent on the amount of daily GNSS-IR sea level retrievals, but they are more suitable for dynamic height correction in storm events than the tidal analysis method. Full article

Long-time coherent integration using frequency hopping signals is a challenging problem for passive bistatic radar due to its frequency hopping characteristics. Apart from range walk, range curve, and Doppler frequency migration, Doppler diffusion caused by frequency hopping characteristics occurs within the observation time, which also lowers the detection performance. To deal with this problem, a novel coherent integration method for frequency hopping signals based on passive bistatic radar is proposed in this paper. In this novel method, range curve and range walk are eliminated by applying generalized Keystone transform. Then, Doppler frequency migration caused by the target’s acceleration is compensated for by a parameter search with a designed search scope. Finally, Doppler frequency migration caused by frequency hopping characteristics is compensated for by designing a new acceleration compensation function and a revised rotation factor for Fourier transform. Since migration effects caused by frequency hopping characteristics are considered and compensated for when using frequency hopping signals, the weak target echo can be better integrated in the observation time compared to when using the existing methods. The simulation results and performance analysis illustrate the effectiveness of the proposed method. Full article

The effects of harmonics, interharmonics, and subharmonics on low-voltage distribution networks, leading to a deterioration in electrical power quality, have become more evident in recent years. The main harmonic sources are power electronic devices due to their implicit nonlinearity. Interharmonic and subharmonic components are mainly caused by a lack of synchronization between the grid frequency and the switching frequency of the power converters. This can be caused by asynchronous modulated devices, or more commonly by fluctuations in the fundamental grid frequency. Interharmonic currents cause interharmonic voltage distortions that affect grid-synchronized or frequency-dependent systems. The IEC-61000-4-7 proposes a general guide on harmonics, interharmonic measurements, and instrumentation in current supply systems. However, the techniques proposed in the standard are intended for measurement and do not enable a precise identification of the interharmonic components in a signal. This work proposes new definitions for the spectral energy aggrupation to improve signal component detection for the IEC standard. Furthermore, an adaptive Kalman filter algorithm is developed that enables the exact identification in real time of the frequency, amplitude, and phase of these components. The proposed system will become the basis for the implementation of a new range of measurement systems that provide improved accuracy and real-time operation. The work is supported by simulated results analysing various scenarios (including transients after changes in harmonic content in the grid voltage) that demonstrate the effectiveness of the proposed method. Full article

We study the nonlinear absorptive and dispersive optical properties of molecular systems immersed in a thermal reservoir interacting with a four-wave mixing (FWM) signal. Residual spin-orbit Hamiltonians are considered in order to take into account the internal structure of the molecule. As system parameters in the dissipation processes, transverse and longitudinal relaxation times are considered for stochastic solute–solvent interaction processes. The intramolecular coupling effects on the optical responses are studied using a molecule model consisting of two coupled harmonic curves of electronic energies with displaced minima in nuclear energies and positions. In this study, the complete frequency space is considered through the pump–probe detuning, without restricting the derivations to only maximums of population oscillations. This approach opens the possibility of studying the behavior of optical responses, which is very useful in experimental design. Our results indicate the sensitivity of the optical responses to parameters of the molecular structure as well as to those derived from the photonic process of FWM signal generation. Full article

The work is devoted to the consideration of methods for determining the strain of objects using fiber Bragg gratings under a high-frequency vibration or pulsed mechanical action, which is difficult to perform using widespread methods and devices. The methods are based on numerical processing of the time dependence of the radiation power reflected from the fiber Bragg grating at various wavelengths, which makes it possible to measure strain parameters in a wide range of magnitude and frequencies. The efficiency of the proposed methods is demonstrated by numerical simulation. It is shown that it is possible to restore the strain dependence on time in the range $\pm 1000$ $\mathsf{\mu }\mathsf{ϵ}$ or more from simultaneously measured power dependencies reflected by the fiber Bragg grating using common fiber-optic components. The case of sequential registration of reflected radiation power at different wavelengths to determine the probability density of the distribution of the strain values is also considered. The results of signal processing obtained both by numerical simulation and experimentally for the case of a linear vibration are presented. The technical problems of using the proposed methods are discussed. Full article

The straddle-type monorail is an electric-powered public vehicle widely known for its versatility and ease of maintenance. The finger-band is a critical connecting structure for the straddle-type monorail, but issues such as loose bolts are inevitable over time. Manual inspection is the primary method for detecting bolt looseness in the finger-band, but this approach could be more efficient and resistant to missed detections. In this study, we conducted a straddle-type monorail finger-band-anomaly-monitoring experiment using Distributed Acoustic Sensing (DAS), a distributed multi-point-monitoring system widely used in railway monitoring. We analyzed track vibration signals’ time-domain and frequency-domain characteristics under different monorail operating conditions. Our findings revealed the following: 1. DAS can effectively identify the monorail’s operating status, including travel direction, starting and braking, and real-time train speed measurement. 2. Time-domain signals can accurately pinpoint special track structures such as turnouts and finger-bands. Passing trains over finger-bands also results in notable energy reflections in the frequency domain. 3. After the finger-band bolts loosen, there is a significant increase in vibration energy at the finger-band position, with the degree of energy increase corresponding to the extent of loosening. Full article

Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain–machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm². Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW. Full article

Growth factor receptor-bound protein 2 (GRB2) is a negative regulator of insulin signaling and a positive regulator of angiogenesis. Its expression is increased in a mouse model of retinal neovascularization and in patients with type 2 diabetes mellitus (T2DM). This case–control study aimed to investigate the association between the rs9896052 polymorphism (A>C) upstream of GRB2 and proliferative diabetic retinopathy (PDR) in patients with T2DM from Southern Brazil, taking into consideration self-reported skin color (white or non-white) and the known duration of diabetes (<10 years or ≥10 years). Genotypes were determined by real-time PCR in 838 patients with T2DM (284 cases with PDR and 554 controls without DR). In the total study group and in the analysis stratified by skin color, the genotype and allele frequencies were similar between cases and controls. However, among patients with less than 10 years of diabetes, the C allele was more frequent in cases than in controls (63.3% versus 51.8%, p = 0.032), and the CC genotype was independently associated with an increased risk of PDR (adjusted OR = 2.82, 95% CI 1.17–6.75). In conclusion, our findings support the hypothesis that the rs9896052 polymorphism near GRB2 is associated with PDR in Brazilian patients with T2DM. Full article

Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault diagnosis method based on vibration signals. Firstly, the vibration signals of each operating state of the motor at different frequencies are measured with vibration sensors. Secondly, the characteristic of Gram image coding is used to realize the coding of time domain information, and the one-dimensional vibration signals are transformed into grayscale diagrams to highlight their features. Finally, the lightweight neural network Xception is chosen as the main tool, and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced into the model to enforce the importance of the characteristic information of the motor faults and realize their accurate identification. Xception is a type of convolutional neural network; its lightweight design maintains excellent performance while significantly reducing the model’s order of magnitude. Without affecting the computational complexity and accuracy of the network, the CBAM attention mechanism is added, and Gram’s corner field is combined with the improved lightweight neural network. The experimental results show that this model achieves a better recognition effect and faster iteration speed compared with the traditional Convolutional Neural Network (CNN), ResNet, and Xception networks. Full article