Ridge Extraction Research Articles

Automatic extraction of Transverse Aeolian Ridges (TARs) and analysis of landform influence for the Zhurong landing area on Mars

The morphology and surface characteristics of Transverse Aeolian Ridges (TARs) reflect the recent and current environmental evolution of Mars. This study investigates the influence of typical Martian non-aeolian landforms impacted on the distribution and morphology of TARs using multi-source high-resolution orbiter images in the Zhurong rover's landing region at the south of Utopian Plain. This study proposes an automatic and effective approach for extracting TARs, while classifying their types and estimating their geometric parameters, including length, width, height, and angle. The obtained results confirm the effectiveness of the proposed method. In the Zhurong rover's landing region, TARs in flat areas have a sparse distribution and simple morphology. However, TARs are more concentrated and diverse in morphology around typical non-aeolian landforms such as cones and troughs. The orientation of TARs is determined by wind direction, and their width is affected by sediment richness. The morphology and distribution of TARs are also influenced by typical non-aeolian landforms, which modify local wind fields and sediments.

Adaptive extraction of characteristic ridges from time-frequency representation for wheelset bearings failure diagnosis under time-varying speed

Instantaneous frequencies-based order tracking is an excellent approach for bearing faults diagnosing at time-varying speeds. The key is to extract ridges from time-frequency representation. However, their precision seriously relies on the cost function and bandwidth selection. To address these issues, an adaptive extraction of characteristic ridges (AECR) method that integrates a variable-bandwidth ridge edge detection (VBRED) and an adaptive exponential cost function (AECF) is presented. VBRED provides a variable-bandwidth search area-based clustering algorithm to isolate neighboring components. AECF can be automatically adjusted based on the signal signature to ensure the smoothness of the ridge. The average frequency ratios of the extracted ridges are used to identify the health conditions of the bearings. Finally, the effectiveness of the proposed AECR is validated by simulations and experiments. The results prove that it performs excellently when the energy and relative position of the ridges are close, compared to other state-of-the-art methods.

IMST-based identification of time-varying cable forces in cable-supported bridges: Numerical, experimental, and field test validation

Accurately assessing the structural integrity of cable-supported bridges hinges upon the precise determination of time-varying cable forces. In this study, a novel method is proposed for identifying these forces, even with the challenge of having only a single accelerometer available. Leveraging an improved Multisynchrosqueezing Transform (IMST) with an efficient ridge extraction algorithm, this method offers a robust solution. The IMST is employed to construct a clearer time-frequency spectrum based on the monitored acceleration response of a cable. Subsequently, the time-frequency ridge extraction algorithm enables the derivation of the cable's instantaneous frequency. Ultimately, the time-varying cable forces are computed by using the axially loaded beam theory and considering the influence of bending stiffness. Validations through a numerical benchmark model and a scaled cable testing confirm the efficacy of this approach. Result indicates that the average error in identifying cable forces is less than 2.50% in the numerical simulation. Moreover, the method is successfully applied to a real-world bridge scenario, demonstrating its ability to accurately evaluate cable forces despite having access to only a single-point noisy acceleration response.

Extraction of instantaneous frequencies for signals with intersecting and intermittent trajectories

A multicomponent signal usually presents multiple trajectories with time-varying frequencies and amplitudes in a time–frequency distribution (TFD). One can extract the ridges corresponding to true signal components and then reconstruct them to recover signal signatures. Most current practices for ridge extraction assume that each trajectory runs throughout the entire time axis without cross-terms. However, this hypothesis is inconsistent with the truth of many measured signals. The increasing application occasions require further consideration of complicated intersecting and intermittent cases. This study addresses this issue and proposes a novel intersecting and intermittent trajectory tracking (IITT) approach. We first develop a data-driven method to effectively isolate peaks from noises in a TFD and generate a dependable peak spectrum. Then, we propose a dynamic optimization tracking function to decide upon the acceptance of the peaks corresponding to an individual component based on the purified spectrum. The IITT approach fully exploits the information from the raw signal without any prior knowledge while promising robustness to the variations of ridge numbers, ridges’ births and deaths, and its continuation and discontinuation. Two simulated and three measured signals are utilized to assess the performance of the proposed IITT. The success elements of the IITT are revealed and discussed in detail at the end of the paper.

A chirplet‐based masking algorithm for smeared spectrum jamming suppression and signal separation

AbstractLinear frequency modulation (LFM) signal is a common radar signal in modern electronic warfare, and smeared spectrum (SMSP) can generate multiple false targets, causing jamming to radar detection. The authors propose a chirplet‐based masking algorithm that can solve the problem of SMSP jamming suppression and address a more complex problem: the separation of jamming signal and multiple LFM signals from intercepted mixed signal. First, the authors obtain matched chirp rates of the source signals through the changing tendency of the Rényi entropy. Then, the ridge of each source signal is extracted from the high‐resolution chirplet transform result using an image processing‐based algorithm. Finally, the jamming and LFM signals are accurately reconstructed through the time‐frequency mask to achieve separation. Even in the extreme case where multiple source signals with close chirp rates are overlapped, the proposed slope‐matching ridge extraction method and iterative update reconstruction method can still achieve commendable signal separation effects. Extensive experimental results demonstrate that the proposed algorithm performs well under extreme conditions of low signal‐to‐noise ratio, high jamming‐to‐signal ratio, and high sea state.

Interference mitigation for FMCW radar via chirp rate estimation and signal separation

As a prevalent system in radar technology, the Frequency modulated continuous wave (FMCW) radar faces significant challenges in interference. This paper proposes a three-step interference mitigation strategy considering the time–frequency (TF) features of interference and time-domain properties of beat signals. Initially, the process involves statistical analysis of chirp rates from high-energy TF points, estimating the matched chirp rates of interference signals. Subsequently, the improved multisynchrosqueezing chirplet transform (IMSSCPT) is suggested for high-resolution TF characterization of interference signals. Utilizing the ridge extraction method, we accurately detect the TF region of interference signals. This precise localization enables the gradual separation of the reconstructed interference from the received signals. Finally, we perform parallel singular value decomposition on Hankel matrices, which are stacked from the mitigated subsequences to obtain the beat subspace and reconstruct the beat signal to reduce the negative effect of noise. A series of experimental comparisons of the proposed algorithm with existing methods have validated its exceptional performance, particularly under conditions of high-level noise.

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Improvements in measurement technology have made it possible to detect problems with rolling bearings more accurately, which is important to ensure that they work properly in mechanical systems under different variable speed conditions. Time–frequency distribution (TFD) methods are widely used in variable-speed rolling bearing fault diagnosis, we construct a new method: adaptive time frequency extraction mode decomposition (ATFEMD) by capturing the distinctive time–frequency information within the TFD through ridge extraction, subsequently, the reconstruction components are further refined into adaptive modes through the harmonic detection and noise testing process. This method is a time–frequency post-processing method that effectively solves the problems of time–frequency energy lack of concentration, poor robustness of instantaneous frequency extraction, and mode aliasing in signal decomposition. This article analyzes the simulated bearing vibration and test bench bearing vibration signals to demonstrate the performance of ATFEMD. Results indicated that the proposed method is characterized by strong robustness, and good feature extraction results compared to other methods.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning

Terrace detection and ridge extraction from high-resolution remote sensing imagery are crucial for soil conservation and grain production on sloping land. Traditional methods use low-to-medium resolution images, missing detailed features and lacking automation. Terrace detection and ridge extraction are closely linked, with each influencing the other’s outcomes. However, most studies address these tasks separately, overlooking their interdependence. This research introduces a cutting-edge, multi-scale, and multi-task deep learning framework, termed DTRE-Net, designed for comprehensive terrace information extraction. This framework bridges the gap between terrace detection and ridge extraction, executing them concurrently. The network incorporates residual networks, multi-scale fusion modules, and multi-scale residual correction modules to enhance the model’s robustness in feature extraction. Comprehensive evaluations against other deep learning-based semantic segmentation methods using GF-2 terraced imagery from two distinct areas were undertaken. The results revealed intersection over union (IoU) values of 85.18% and 86.09% for different terrace morphologies and 59.79% and 73.65% for ridges. Simultaneously, we have confirmed that the connectivity of results is improved when employing multi-task learning for ridge extraction compared to directly extracting ridges. These outcomes underscore DTRE-Net’s superior capability in the automation of terrace and ridge extraction relative to alternative techniques.

Rolling Bearings Fault Diagnosis under Variable Speed Conditions Based on Multitime‐Frequency Ridge Extraction

The vibration signals of rolling bearings under variable speed conditions exhibit time‐varying, nonstationary, and nonlinear characteristics, making it challenging to extract fault features. A fast path optimization‐based multitime‐frequency ridge extraction (FPOMRE) method is proposed in this paper to address the problem. First, the time‐frequency representation of the fault signal is obtained through the adaptive short‐time Fourier transform (STFT), and then the FPOMRE algorithm is used to extract additional ridges below the lowest time‐frequency ridge multiple times. The frequency ratio of each ridge to the additional ridge, the average value of the ratio, and the variance of the ratio are calculated, respectively, and finally, the matching degree between the average ratio and the fault feature coefficient is compared with the diagnosis of the bearing fault. The simulation and experimental results show that the FPOMRE method can effectively diagnose the bearing fault under constant speed and variable speed conditions, and the extraction accuracy of time‐frequency ridges under constant speed is higher than that under variable speed, and the extraction accuracy under high speed is higher than that under low speed. The relative error of extracting rotation frequency under constant speed and variable speed is 0.5% and 1.3%, respectively. In addition, comparing the FPOMRE with the maximum amplitude method (MAM), both FPOMRE and MAM can effectively extract the instantaneous shaft rotation frequency at a constant speed. However, under variable speed conditions, the ridge extracted by FPOMRE has a higher accuracy and lower average processing time than that extracted by MAM.

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Rotating machinery fault signals often consist of multiple components with time varying frequencies under variable speed conditions. Spectral overlap exists among these components, making it difficult to independently separate the features of the components. Singular spectrum decomposition (SSD), a singular spectrum analysis-based signal decomposition method, has shown its great potential in suppressing background noise and extracting fault-related components in complex background noise environments. However, SSD is a frequency domain decomposition method with equivalent filtering characteristics, and it is susceptible to the mode mixing when processing signals with spectral overlap. Moreover, the choice of a key parameter in the iteration decomposition process of SSD, the embedding dimension, is determined using an empirical formula, which might cause suboptimal decomposition outcomes. To address these issues, this paper proposes a generalized adaptive singular spectrum decomposition (GASSD) method, which combines generalized demodulation with improved embedding dimension selection for SSD. GASSD incorporates SSD into the framework of adaptive generalized demodulation to separate specific frequency domain features. Firstly, for an effective generalized demodulation analysis, a region block synchronous ridge extraction method is proposed to accurately estimate the instantaneous frequency ridges from the time-frequency plane, which helps construct proper demodulation phase functions. Secondly, to achieve optimal analysis of SSD, a Gini moderation decomposition index is designed to improve the construction of the trajectory matrix by determining an appropriate embedding dimension. Finally, the reliability of the proposed method is demonstrated by analyzing wind turbine generator bearing fault signals and rotor rubbing fault signals.

A time–frequency ridge extraction diagnostic method for composite faults of bearing gears in wind turbine gearboxes

The bearing and gear faults in the gearbox interact with each other, and the weak fault characteristics are often masked by the strong fault characteristics, making it difficult to accurately extract complete fault information. To solve this problem, a time–frequency ridge extraction diagnosis method based on multisynchrosqueezing transform (MSST) is proposed. This method utilizes MSST to enhance the compound fault features, especially the gear fault amplitude modulation components. It also utilizes the time–frequency ridge extraction method to separate the gear fault amplitude modulation components and the bearing fault impact pulse components. Additionally, it uses time shifting to substitute data and verifies the independence of the harmonic zero hypothesis to determine the accuracy of the fault components. This method provides a favorable basis for the extraction and identification of compound faults, especially weak faults, in complex dynamic signals of the gearbox. The effectiveness of this method is validated through simulation examples and practical applications.

Automated and Adaptive Ridge Extraction for Rotating Machinery Fault Detection

A ridge in a time-frequency graph (TFG) describes the relationship of a signal component's instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for assessing machine health conditions without rotational speed measurement. This article proposes a new automated and adaptive ridge extraction (AARE) method. The AARE develops an adaptive edge detection strategy to avoid excessive interferences when searching for a ridge. Besides, the AARE creates a balance between exploring peak amplitudes and guaranteeing a continuous curve through an adaptive core function, which is constructed entirely based on the instantaneous characteristics of the analyzed signal. The unique advantage of the proposed method is that it dispenses with the tuning of parameters and runs automatically. Thus, human intervention is minimized. Gear and bearing vibration signals collected under variable speed conditions are applied to investigate and demonstrate the performance of AARE. In addition, some challenging cases are analyzed and discussed. Results show that AARE has a superior performance in ridge extraction compared with the reported approaches.

Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity

The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1 ± 6.9 years) and 24 older participants (64.9 ± 6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼ 0.1 Hz is significantly reduced in the older adults in 46/176 fNIRS-EEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease.

Use of vibration signal to estimate instantaneous angular frequency under strong nonstationary regimes

Instantaneous angular frequency (IAF) is an inherent characteristic associated with mechanical dynamics and contains adequate diagnostic information about rotating machinery, especially under speed fluctuation conditions. However, not all machines have the facility to install tachometers or encoders for tracking the instantaneous angular speed (IAS). An alternate solution is using vibration measurements and recovering the missing speed by exploring the acceleration signal. Thus, accurate IAF estimation is crucial to the consequent fault diagnosis. To date, multiple methods have been developed to address this issue, but most present poor results when dealing with the significant variations of IAS, which are common in practical applications. To overcome this deficiency, a new method consisting of time–frequency ridge extraction and an improved multi-order probabilistic approach is proposed in the paper. The described method fully exploits the information within the vibration response while promising robustness to the variations of the running regimes. The proposed method is demonstrated by assessing its performance on three benchmark data sets and two experimental signals collected from our laboratory and comparing them with five advanced IAF estimation methods.

Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53–24.57 and 18.57–26.92. Both the quantitative index and the time–frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters.

A CSAR 3D Imaging Method Suitable for Edge Computation

Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).

A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification.

Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal's envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.

A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar

The existing research proposes an intra-pulse frequency agile radar waveform with “active” anti-jamming characteristics. It uses the discontinuity and periodicity of the interrupted-sampling repeater jamming and combines the anti-jamming algorithm to effectively suppress interrupted-sampling repeater jamming. In order to improve the jamming effectiveness of the interferer for the intra-pulse frequency agile waveform, this paper proposes to jam the intra-pulse frequency agile radar by using a non-uniform interrupted-sampling and forwarding method under parameter constraints. The proposed method first obtains the sub-pulse width of the intra-pulse frequency agile radar waveform by parameter estimation of the intercepted intra-pulse frequency agile radar signal through time–frequency ridge extraction and wavelet transform. Then, we construct non-uniform interrupted-sampling repeater jamming based on sub-pulse width constraint interference parameters. Theoretical analysis and results show that the non-uniform interrupted-sampling forwarding under parameter constraints makes it challenging to suppress interference in multiple domains, such as the time–frequency and pulse compression domain for intra-pulse frequency agile radar, which significantly improves the jamming capability of the jammer for intra-pulse frequency agile radar.

Fringe pattern demodulation using the fan two-dimensional continuous wavelet transform conjugate to Shannon entropy.

This paper proposes an algorithm for phase demodulating fringe patterns using a two-dimensional continuous wavelet transform (2D-CWT). This algorithm exploits the isotropy property of the fan mother wavelet conjugated to Shannon entropy to perform the ridge extraction process by using only 2D-CWT arguments. The proposed algorithm's performance is shown through simulated fringe patterns corrupted by speckle noise. Also, to evaluate the accuracy of the ridge extracted from the modulus to that obtained from the arguments of 2D-CWT, the developed algorithm is compared to the maximum ridge extraction algorithm for 2D-CWT and cost function ridge extraction algorithm for 2D-CWT, which extract the ridge from the 2D-CWT modulus. Furthermore, we demonstrate the ability of the proposed algorithm to demodulate real fringe patterns derived from optical metrology for temperature measurement. The most important result of the proposed method is that it is provably optimal in estimating the 2D-CWT ridge of oriented fringes. The added strength of the algorithm is that it is simpler and has better resistance to speckle noise than previous methods because it employs scalograms of arguments, which are the origin of the definition of the ridge.