Signal Reconstruction Method Research Articles

Energy management strategy for fuel cell hybrid tractor considering demand power frequency characteristic compensation

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor’s traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750 g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery.

Acoustic Signal Reconstruction Across Water–Air Interface Through Millimeter-Wave Radar Micro-Vibration Detection

Water surface micro-amplitude waves (WSMWs) of identical frequency are elicited as acoustic waves propagating through water. This displacement can be translated into an intermediate frequency (IF) phase shift through transmitting a frequency modulated continuous wave (FMCW) towards the water surface by a millimeter-wave radar, and information transmission across the water–air interface is achieved via the signal reconstruction method. In this paper, a novel mathematical model based on energy conversion from underwater acoustic to vibration (ECUAV) is presented. This method was able to obtain WSMW vibration information directly by measuring the sound source level (SL). An acoustic electromagnetic wave-based information transmission (AEIT) system was integrated within the water tank environment. The measured distribution of SL within the frequency range of 100 Hz to 300 Hz exhibited the same amplitude variation trend as predicted by the ECUAV model. Thus, the WSMW formation process at 135 Hz was simulated, and the phase information was extracted. The initial vibration information was retrieved through a combination of phase unwinding and Butterworth digital filtering. Fourier transform was applied to the vibrational data to accurately reproduce the acoustic frequency of underwater nodes. Finally, the dual-band binary frequency shift keying (BFSK) modulated underwater encoding acoustic signal was effectively recognized and reconstructed by the AEIT system.

Sequence signal prediction and reconstruction for multi-energy load forecasting in integrated energy systems: A bi-level multi-task learning method

Multi-energy load forecasting is the basis for the optimization and scheduling of integrated energy systems (IES). An IES contains much heterogeneous energy with volatility and temporality, and complex coupling relationships between the energy sources, which makes it difficult to accurately predict multiple loads simultaneously. Considering these issues, this paper proposes a bi-level multi-task learning (BiMTL) method for simultaneous sequence signal prediction and reconstruction via deep neural networks. In the feature construction process, considering the volatility and randomness of the load, the improved wavelet packet decomposition model is constructed to decompose the multiple load sequences into sub-signal with more obvious signal characteristics, and improves the ability of the forecasting model to capture and respond to the fluctuations of load sequences within a local range. In the modeling process, considering the excessive dependence of traditional signal reconstruction methods on the prediction results of sub-bands, this paper innovatively proposes a BiMTL method. This method employs a second-layer shared learning to perform signal reconstruction and error correction on the output of the first layer’s sub-band predictions. Finally, a BiMTL-LSTM model is constructed to extract the coupling and temporality of loads based on the LSTM hard-sharing mechanism. Experimental results show that the proposed method outperforms the current state-of-the-art methods on the widely used Tempe dataset, achieving an accuracy of up to 98.70%.

Research on signal enhancement and flow rate calculation methods of multi-channel ultrasonic flowmeter

Abstract In the field of drilling, accurate measurement of mud flow helps detect potential leaks or abnormal situations, thereby improving overall safety and preventing accidents from occurring. Traditional contact flow meters require breaking the pipeline before installation, making them difficult to install in underground environments. In addition, the complex composition of underground fluids and their corrosive nature can also lead to unsatisfactory measurement results. Therefore, as a non-invasive flow measurement tool, ultrasonic flowmeter is a better choice. However, the ultrasonic echo signals of current ultrasonic flow meters are susceptible to external interference in actual testing, resulting in a decrease in signal quality and making it impossible to perform accurate detection. This article focuses on the research of ultrasound echo signal processing model, designs a four channel ultrasound transceiver model, improves the wavelet denoising algorithm, designs signal reconstruction and baseline correction processing methods, and proposes a pipeline flow calculation algorithm based on Gaussian numerical integration. This method can greatly improve the quality of ultrasound echo signals and the average accuracy of flow measurement. Finally, relevant testing experiments were conducted, and the results of the final performance measurement showed that the method proposed in this paper improved the average accuracy of the measurement by 5.07%, and the relative error remained at around 2%, with a significant improvement in error rate. Therefore, the method proposed in this article has extremely high accuracy in mud flow measurement.

Comprehensive Analysis and Reconstruction of Sensor Faults in Interleaved Buck Converters Using Sliding Mode Observers

This paper presents a fault signal reconstruction method for current sensors in an interleaved buck DC–DC converter, utilizing a sliding mode observer (SMO). A filter bank is used to design the observer within an extended-order system, effectively treating sensor faults as actuator faults, which enables precise estimation of the fault signal. Thus, the proposed approach allows for the identification of the faulty sensor and supports the implementation of fault-tolerant strategies. The paper provides an in-depth analysis of current sensor faults, verifies their impact on current balancing control, and demonstrates the challenge of achieving error-free current estimation in one phase using observers. A comprehensive set of simulation results is carried out, validating the method’s effectiveness and showing a strong correlation with theoretical principles.

Loose particle Detection: The optimal detection condition and weak loose particle impulse extraction

To achieve effective detection of loose particles within small cavities in microelectronic devices, this paper proposes the optimal detection conditions of loose particles in ceramic packaging for microelectronic devices based on simulation, and presents a method for weak signal reconstruction using weighted sparse representation. To address the challenge of ineffective detection of loose particles under recommended vibration conditions, a particle-cavity collision dynamics model is established. Subsequently, the detection laws of loose particles under different accelerations, frequencies, and cavity heights are studied through simulation, leading to the establishment of optimal detection conditions for microelectronic devices with varying cavity heights. To address the issue of weak impulses being submerged in background noise under optimal detection conditions, sparse representation is utilized for signal reconstruction. First, a Laplace wavelet dictionary is constructed according to the impulse characteristics. Second, the generalized minimax-concave (GMC) penalty function is applied as a sparse regularization term to preserve signal amplitude. Meanwhile, a weight matrix based on kurtosis and singular value is designed to threshold sparse coefficients. The results demonstrate that the proposed optimal detection conditions and signal reconstruction method effectively detect loose particles. Compared with other algorithms, the proposed method reduces background noise in the particle impact noise detection (PIND) signals while maintaining impulse amplitude; it also improves signal reconstruction accuracy and offers valuable insights into effective loose particle detection in microelectronic packaging devices.

Wavelet-based spatiotemporal sparse quaternion dictionary learning for reconstruction of multi-channel vibration data

Accurate reconstruction of multi-source data information plays an essential role in remote intelligent operation, and prognostic and health maintenance (RIO-PHM) of industrial systems. However, traditional signal reconstruction methods such as compressed sensing fail to capture the spatio-temporal characteristics and coupling nature of the multi-source or multi-channel data. To alleviate this bottleneck, in this paper, a new wavelet-based spatiotemporal sparse quaternion dictionary learning (WSTS-QDL) algorithm is formulated for the reconstruction of multi-channel vibration data for the first time. Specifically, the wavelet-based spatiotemporal sparse low-rank matrix (SLRM) algorithm is elaborated and the sparse coefficient matrix of the multi-channel signals can be estimated using the split Bregman iteration (SBI) technique. Then, quaternion-based dictionary learning is introduced for multi-channel data reconstruction according to the updated sparse coefficient matrix during quaternion dictionary learning. Eventually, two equipmental scenarios including run-to-failure data of rolling bearing in bearing test bench and vibration signal of the failured bearing in corn thresher, are utilized for verification. The experimental results demonstrate that the highest recovery accuracy performance with scoring function and the lowest errors are achieved by the proposed method in contrast with the state-of-the-art benchmarks such as orthogonal matching pursuit (OMP) method and spatiotemporal sparse Bayesian learning (SSBL), and the waveform of phase space trajectory and points cloud distribution of the Poincare section in the original signal are similar/same to that obtained by the proposed method.

NEW METHODS OF DIGITAL SIGNAL PROCESSING

Digital Signal Processing (DSP) is a crucial field of research and study aimed at manipulating digital signals to enhance their accuracy, reliability, and efficiency. Over the past 5-10 years, advancements in DSP technology have yielded high performance across various fields including telecommunications, audio processing, medical imaging, and more. Recent innovations continue to expand the capabilities and applications of DSP. This paper highlights several new methods and trends in DSP that represent significant achievements in the field. Examples include: “integration of machine learning and deep learning,” “rare imaging and compression sensitivity,” “quantum signal processing,” “graph signal processing,” “advanced methods for signal reconstruction,” “real-time DSP in embedded systems,” and more. Overall, the field of digital signal processing is rapidly evolving, with new methods and technologies emerging to address the challenges of the digital world. The integration of machine learning, exploration of quantum computing, and adaptation to complex data structures are just a few directions in which DSP is expanding. As these new methods are refined and widely adopted, they are bound to open up new opportunities across a wide range of disciplines and fields.

Multi-modal signal adaptive time-reassigned multisynchrosqueezing transform of mechanism

High-end mechanical equipment often operates under non-stationary conditions, such as varying loads, changing speeds, and transient impacts, which can lead to failures. Time-frequency analysis (TFA) integrates time and frequency parameters, allowing for detailed signal analysis and is widely used in this context. To improve the accuracy of assessing the operational status of mechanical equipment, this paper proposed a multi-modal signal adaptive time reassignment multiple synchrosqueezing transform (MSST) TFA method. This method enhances the MSST method by using a local maximum technique to address energy ambiguity in TFA. Additionally, the optimal window width for each function is determined through iterative processes to better concentrate energy in the TFA. Multi-modal signals are jointly analyzed using an impulse feature extraction method for signal reconstruction, enabling multi-dimensional fault analysis. The proposed method is validated with both simulation and experimental data from a planar parallel mechanism (PPM) and is compared against classical and advanced techniques. The results show that the method effectively captures shock features in multi-modal signals, offering a more consolidated time-frequency representation (TFR) than existing TFA algorithms.

Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning

To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD)-based signal reconstruction method is first introduced to extract features representing motor bearing faults. A feature matrix construction method based on improved information entropy is then proposed to quantify these fault features. Finally, a fault diagnosis algorithm architecture integrating a multi-scale convolutional neural network (MSCNN) with attention mechanisms and a bidirectional long short-term memory network (BiLSTM) is developed. The experimental results for four fault states show that this model can effectively extract fault features from original vibration signals and, compared to other fault diagnosis models, offer high diagnostic accuracy and strong generalization, maintaining high accuracy even under varying speeds and noise interference.

Automated CFRP impact damage detection with statistical thermographic data and machine learning

The study is focused on the use of machine learning models for the automated detection of impact damage in carbon fiber reinforced polymer (CFRP) by flash-pulse thermographic testing. A new method for thermographic data pre-processing, which is based on statistical features, was proposed. Nine machine learning models for the automated detection of impact damage in CFRP samples were applied to the raw thermographic data, data pre-processed by the suggested method and data pre-processed by the widely used thermographic signal reconstruction (TSR) method. The machine learning models were tested to provide a binary classification of impact damage in CFRP. The results presented in this study show improved performance of the classification if the data are pre-processed by the proposed method. The best results were obtained by a Bagged tree ensemble trained with statistical features. The final balanced accuracy achieved for the Bagged trees model trained on 40 statistical features was 99.8 % which indicates a very good performance.

Separation of overlapping non-stationary signals and compressive sensing-based reconstruction using instantaneous frequency estimation

Compressive sensing uses an incomplete sample set to consider signal reconstruction with sparse basis representations over a specific transform domain. This work examines the difficulties encountered in various signal processing applications, such as data overlap with the target signal in time and frequency domains. The conventional filtering and windowing processes fail to promote better results in recovering the desired signal. The non-stationary signal with time-frequency representations is highly significant for certain valuable applications. An efficient signal separation and reconstruction method based on instantaneous frequency estimate is proposed to improve the non-stationary signal outcome. The proposed research includes speech signal data acquisition, signal component decomposition, instantaneous frequency estimation, overlapped signal separation, and reconstruction. Two datasets, Dacon Overlapped Speech and LJ Speech Dataset, are utilized to generate a new overlapped signal dataset. Initially, the generated speech signals are collected and decomposed into several monocomponents using Synchrosqueezing Wavelet Transform (SynWav), Renyi's quadratic entropy (RQuad), Narrow bandpass filtering (NBand) and single frequency filtering (SFreq). The instantaneous frequencies are estimated using the Hilbert-Huang transform (HilHuT) to generate a time-frequency representation of multi-component signals. The overlapped signals are separated and reconstructed to a desired signal using a Compressive sensing-based Discrete Cosine transform with amended intrinsic chirp separation (CDcT_AIChirS). The proposed model is simulated using PYTHON to examine the performances. In comparison, the mean square error is calculated to be 2.71, the root mean square error is calculated to be 1.64, and the signal to noise ratio is calculated to be 32dB.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer.

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.

Performance monitoring and diagnosis of bridge structures based on time multiscale divide and conquer strategy

During the service life of bridge structures, they will be affected by various physical and environmental changes, which comprehensively manifest on different time scales. In other words, structural effects will have different variation patterns at different time scales, which will be reflected in the multi-scale characteristics of structural mechanical properties over time. However, most of the existing bridge performance indicators are defined on a time scale corresponding to a single factor. Therefore, it is necessary to analyze, process, and apply bridge monitoring signals differently at different time scales and components of different factors. This article proposes a signal decomposition and reconstruction method that extracts signals of different components from strain monitoring signals of bridge structures. On this basis, different feature extraction and indicator recognition are carried out for different extracted components. Among them, for component A related to bridge vibration, this paper proposes a strain mode identification method based on statistical stability. This method is based on the assumption that the strain mode under random noise has approximate time invariant characteristics, and noise is eliminated through statistical averaging. The component B of the vehicle induced effect is obtained through empirical mode decomposition (EMD) and bandpass filtering, and the lateral collaborative performance of the assembled beam bridge is characterized based on the correlation coefficient of component BA bridge hinge joint damage identification method based on temperature strain is proposed for component C of temperature effect. The effectiveness and feasibility of the above workflow were verified through real bridge data.

Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD

Abstract In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost.

IGFT-MHCNN: An intelligent diagnostic model for motor compound faults based decoupling and denoising of multi-source vibration signals

In view of the insufficient representation of single source signals for multi-point compound fault and the nonlinear strong coupling of vibration signals in motor transmission system, the inverse graph Fourier transform and improved multi-head convolutional neural network (IGFT-MHCNN) diagnostic model–based decoupling and denoising of multi-source vibration signals is proposed. The MHDCNN is constructed by improved multi-head convolution network and multi-label decoupling classifier, in which they extract multi-source signal features and decouple compound fault label information, respectively. Due to strong noise in compound fault and mapping capability of graphic signal processing method for complex data, a signal reconstruction method based on IGFT is established to remove residual noise from multi-source signals and enhance the main feature components. Two experimental cases show that the constructed model can effectively reduce vibration noise and achieve multi-label decoupling and identification of motor compound faults with strong coupling.

Multi-scale reconstruction of rolling mill vibration signal based on fuzzy entropy clustering

The mill vibration signals collected during mill operation are often affected by complex working conditions and various disturbances, thus presenting strong non-stationary characteristics. After the vibration signal is processed by noise reduction, although the noise interference and modal aliasing phenomenon are solved, the extracted vibration signal of the rolling mill still has the problems of strong non-stationarity, high complexity, and great difficulty in analyzing, which largely affects the prediction model construction. To address the above problems, this study proposes a multi-scale signal reconstruction method based on Fuzzy Entropy and Gath-Geva fuzzy clustering algorithm, which effectively reduces the complexity of the original sequence, reduces the number of predictive modellings, and improves the signal predictability and prediction accuracy. The sequence reconstruction of such non-stationary vibration signals based on their internal characteristics has obvious advantages for the problem of mill vibration prediction and analysis.

Passive radar direct wave signal reconstruction method based on fractional domain blind source separation

Passive radar direct wave signal reconstruction method based on fractional domain blind source separation

Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS).

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b-values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.