Synchrosqueezed Wavelet Transform Research Articles

A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

Analysis of Wavelet Coherence in Calf Agonist-Antagonist Muscles during Dynamic Fatigue.

Dynamic muscle fatigue during repetitive movements can lead to changes in communication between the central nervous system and peripheral muscles. This study investigated these changes by examining electromyogram (EMG) characteristics from agonist and antagonist muscles during a fatiguing task. Twenty-two healthy male university students (age: 22.92 ± 2.19 years) performed heel raises until fatigue. EMG signals from lateral gastrocnemius (GL) and tibialis anterior (TA) muscles were processed using synchrosqueezed wavelet transform (SST). Root mean square (RMS), mean frequency (MF), power across frequency ranges, wavelet coherence, and co-activation ratio were computed. During the initial 80% of the task, RMS and EMG power increased for both muscles, while MF declined. In the final 20%, GL parameters stabilized, but TA showed significant decreases. Beta and gamma intermuscular coherence increased upon reaching 60% of the task. Alpha coherence and co-activation ratio remained constant. Results suggest that the central nervous system adopts a differentiated control strategy for agonist and antagonist muscles during fatigue progression. Initially, a coordinated "common drive" mechanism enhances both muscle groups' activity. Later, despite continued increases in muscle activity, neural-muscular coupling remains stable. This asynchronous, differentiated control mechanism enhances our understanding of neuromuscular adaptations during fatigue, potentially contributing to the development of more targeted fatigue assessment and management strategies.

Dynamic inspection data-based analysis of rail base metal irregularities for engineering failure prevention

With the continual increase in train speeds, the requirement for track smoothness has become increasingly critical. Within the railway system of China, the prevalence of short-wave defects, attributed to the rail base metal irregularity, adversely affects the stability of train movement and constitutes a considerable safety risk. However, research into the rail base metal irregularity remains markedly scant, with a conspicuous absence of effective evaluation and identification methodologies. This deficiency critically restricts the capability to mitigate short-wave disturbances arising from such irregularities, negatively impacting the safety and operational stability of railway systems. Therefore, this paper proposes a method for evaluating and identifying the rail base metal irregularity based on dynamic inspection data. Initially, this paper conducts a comprehensive assessment of rail base metal irregularities by analysing wavelength characteristics, frequency amplitude features, and dynamic responses of vehicles. Subsequently, this paper utilises the Synchrosqueezed Wavelet Transform (SWT) technique, which refines the frequency resolution by compressing frequency components within the frequency domain, eliminating cross-frequency interference, and minimising energy dispersion across scale domains, thus accurately delineating the time–frequency attributes of rail base metal irregularity. Building upon the foundation provided by SWT, this paper introduces an identification method employing the Summed Wavelet Power (SWP) across various frequency bands. This approach evaluates the energy variations of different wavelength components relative to changes in mileage, facilitating the identification of track sections with rail base metal irregularity. The efficacy of the proposed methodology is corroborated through the analysis of actual measurement data. The results show that the SWT and SWP techniques can effectively identify rail base metal irregularity, thereby offering substantial support for enhancing the safety and comfort of railway operations.

Unified theory for identifying vertical and radial damping ratios of curved bridges by two connected scanning vehicles

A unified theory is presented for identifying the vertical and radial damping ratios of a horizontal curved bridge by the correlation between two connected scanning vehicles using the variational mode decomposition (VMD) and synchrosqueezed wavelet transform (SWT). Firstly, closed-form solutions are derived for the out-of-plane and in-plane responses of the curved beam caused by the gravitational and centrifugal forces, respectively, of the two moving vehicles. Then, generalized contact formulas are presented for calculating the vertical and radial responses of the vehicle-bridge contact points from the vehicle’s responses, to avoid vehicle frequencies’ masking effect. Subsequently, the instantaneous amplitudes of the component responses of the curved bridge are obtained by the VMD-SWT technique. Finally, generalized damping formulas for retrieving the vertical and radial damping ratios of the curved bridge are established by utilizing the spatial correlation between the two vehicles. This paper has demonstrated that: (1) the vertical and radial damping ratios of the curved bridge can be successfully and precisely identified; (2) the generalized damping formula for curved beams has been attested to be robust against various factors; and (3) the adverse effect of road roughness on damping ratio recovery can be mitigated by the aid of accompany traffic.

Effect of the void and crack quantity and location on ultrasound: resin experiments and carbon brick validation

ABSTRACT In industrial applications, there is a lack of methods applicable to large-scale, rapid and non-destructive detection of voids and cracks inside carbon bricks. This paper used the ultrasonic testing (UT) A-scan method to investigate the influence of quantity (void fraction:1.4%-4.6%; number of artificial cracks:1–7) and location (five layers) of voids and cracks inside the resin on ultrasonic waves. Both fast Fourier transform (FFT) and synchrosqueezed wavelet transform (SWT) are employed to analyse the amplitude, main frequency and normalised peak time of ultrasonic waves. The results indicate that increased void fraction and number of cracks lead to greater amplitude attenuation, promoting the transition from the fundamental wave to harmonics. Void and crack location symmetrically affect ultrasound amplitude, main frequency, and normalised peak time around the sample centre. Meanwhile, fitting equations are established for the main frequency of the fundamental wave (f 1) in relation to defect quantity. A high-precision quantitative defects detection standard is also developed based on normalised peak time. The validation based on carbon bricks indicates that the derived empirical relations between these parameters and defects can lay the foundation for large-scale and rapid non-destructive detection in carbon bricks.

A Sympathetic Inrush Identification Method Based on the Time-Frequency Energy

Sympathetic inrush is a phenomenon caused by the energization of the neighboring unloaded transformer. It may cause current differential protection to operate in the wrong way. Due to the lack of a special and continuous identification method, this paper studies the inherent feature of the two transformers during sympathetic inrush. Then a substation-area current signal based on the feature is constructed and its characteristics are analyzed. The time-frequency energy of the current signal is extracted based on the synchrosqueezed wavelet transform (SWT) since the SWT can process the transient signals accurately. The extracted energy is taken as the basis of the identification criterion. The simulation results conducted by PSCAD/EMTDC software prove the accuracy and effectiveness of the proposed method.

Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

Abstract Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

Time frequency domain deep CNN for automatic background classification in speech signals

Many application areas, such as background identification, predictive maintenance in industrial applications, smart home applications, assisting deaf people with their daily activities and indexing and retrieval of content-based multimedia, etc., use automatic background classification using speech signals. It is challenging to predict the background environment accurately from speech signal information. Thus, a novel synchrosqueezed wavelet transform (SWT)-based deep learning (DL) approach is proposed in this paper for automatically classifying background information embedded in speech signals. Here, SWT is incorporated to obtain the time-frequency plot from the speech signals. These time-frequency signals are then fed to a deep convolutional neural network (DCNN) to classify background information embedded in speech signals. The proposed DCNN model consists of three convolution layers, one batch-normalization layer, three max-pooling layers, one dropout layer, and one fully connected layer. The proposed method is tested using various background signals embedded in speech signals, such as airport, airplane, drone, street, babble, car, helicopter, exhibition, station, restaurant, and train sounds. According to the results, the proposed SWT-based DCNN approach has an overall classification accuracy of 97.96 (± 0.53)% to classify background information embedded in speech signals. Finally, the performance of the proposed approach is compared to the existing methods.

Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence

The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.

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.

Recovering mode shapes of curved bridges by a scanning vehicle

The contribution of this paper is to recover the vertical and radial mode shapes of curved bridges from the contact responses of a single-axle scanning vehicle using the variational mode decomposition (VMD) and synchrosqueezed wavelet transform (SWT) for the first time. To catch both the vertical and radial vibrations of the curved bridge, the test vehicle is modeled as a three degree-of-freedom (DOF) system involving the vertical, rocking, and lateral (radial) displacements. Firstly, closed-form solutions for the out-of-plane and in-plane vibrations of the curved beam under the vehicle's action are derived. Then, the unified formula is derived for calculating the vertical and radial contact responses of the single-axle test vehicle. Finally, the VMD technique is employed to process the vertical and radial contact responses to obtain the component responses, followed by the SWT to generate the mode shapes. The numerical analysis demonstrated that: (1) the unified formula newly derived is good for both vertical and radial responses; (2) more bridge frequencies can be extracted from the contact than vehicle response; (3) the vertical and radial mode shapes of the curved bridge can be successfully recovered by the proposed combined VMD-SWT technique against various factors; and (4) with the aid of random traffic, the adverse effect of pavement roughness on mode shape recovery is alleviated.

Synchrosqueezed Fractional Wavelet Transform: A New High-Resolution Time-Frequency Representation

The synchrosqueezed wavelet transform (SSWT) has been proven to be a powerful time-frequency analysis tool. However, this transform is unable to deal with signals with fast varying instantaneous frequencies. The objective of this paper is to overcome this deficiency using the fractional wavelet transform (FRWT), which is a generalization of the conventional wavelet transform. We first propose a synchrosqueezed FRWT (SSFRWT), which shares many properties of its SSWT counterpart while offering attractive new features. Then, we present a theoretical analysis of the SSFRWT, including the derivation of its basic properties. Moreover, we show that the discrete form of the SSFRWT admits efficient numerical implementation akin to that of the SSWT. Finally, the theoretical derivations are validated via simulations.

Intelligent Fault Quantitative Identification via the Improved Deep Deterministic Policy Gradient (DDPG) Algorithm Accompanied With Imbalanced Sample

The imbalanced amount of faulty and normal samples seriously affects the performance of intelligent fault diagnosis models. Aiming to solve the above problem, an improved deep deterministic policy gradient algorithm (ResDPG) based on actor-critic architecture is proposed. In ResDPG, a multi-channel time-frequency representation (TFR) is obtained by the synchrosqueezed wavelet transform (SWT) to avoid the non-stationary of the original signal. ResNet is introduced to construct an actor network for extracting representative deep fault features to improve the accuracy of fault diagnosis, while AlexNet is utilized to build a critic network and guide the actor to train in the right direction according to the evaluation mechanism. The model constructs a rational and practical reward function based on the imbalance ratios and uses the minimum distance between the centers of the classes as the feedback of the reward. The optimized state transfer function improves the learning frequency of minority classes. Verified via two datasets of rolling bearing, ResDPG can independently and autonomously achieve accurate fault quantitative identification with high efficiency, stability, and generalization. It also achieves state-of-the-art performance under unbalanced data and variable load.

Wet Gas Two-Phase Flow Measurement of Swirl Flowmeter Based on Synchrosqueezed Wavelet Transform

Wet Gas Two-Phase Flow Measurement of Swirl Flowmeter Based on Synchrosqueezed Wavelet Transform

A Generalized Classification Framework for Power Quality Disturbances Based on Synchrosqueezed Wavelet Transform and Convolutional Neural Networks

A Generalized Classification Framework for Power Quality Disturbances Based on Synchrosqueezed Wavelet Transform and Convolutional Neural Networks

Dynamic wavelength calibration based on synchrosqueezed wavelet transform.

With the developments of the tunable laser source (TLS), there are increasing demands for high-resolution dynamic wavelength calibration in recent years. Considering mutual constraints between wide measurement range and high calibration resolution, we propose a dynamic wavelength calibration method based on an auxiliary Mach-Zehnder interferometer (MZI) and the synchrosqueezed wavelet transform (SSWT). Our proposed method can achieve a calibration resolution of 5 fm and a tuning range of 10 nm. Moreover, the measurement range and spatial resolution of the optical frequency domain reflectometer (OFDR) system are improved to ∼80 m and ∼mm, respectively. Our proposed approach can substantially reduce the subtle spectrum distortion (tens of fm) in coherent optical spectrum analyzer (COSA) systems.

PFMD: A Power Frequency Magnetic Anomaly Signal Detection Scheme Based on Synchrosqueezed Wavelet Transform

Magnetic anomaly signal detection (MASD) is a passive method for the detection of visually obscured ferromagnetic objects. This paper proposes a new method for MASD based on the ambient field of power frequency magnetic (power transmission line system). Moreover, a new information extraction technique is extended by employing Synchrosqueezed Wavelet Transform (SSWT) to improve the accuracy of the MASD method. With the extended information extraction technique, the time-frequency information of the anomalies can be efficiently distinguished from the power frequency magnetic anomaly signal. The multi-component of the time-frequency information is separated by extracting the time-frequency ridges in the spectrogram. The complexity of time-frequency information is evaluated using Rayleigh entropy. Compared with the continuous wavelet transform and short-time Fourier transform, the Rayleigh entropy of our method is reduced by 4.1886 and 4.3623, respectively. Finally, the efficiency of the new method is verified by the outfield experiments.

New adaptive robust [formula omitted] control of smart structures using synchrosqueezed wavelet transform and recursive least-squares algorithm

Two kinds of uncertainties, one due to the dynamic earthquake loads with a wide frequency band and the other due to structural parameters exist in large and complex real-life structures. Most existing control algorithms consider only one of them, resulting in difficulty to guarantee necessary control performance for large complex structures, such as better vibration suppression on structural peak response and robustness performance. Considering the two uncertainties simultaneously, in this paper, a new adaptive robust H∞ control methodology is presented for vibration control of structures through adroit integration of synchrosqueezed wavelet transform (SWT) and recursive least-squares (RLS) algorithm. The robust H∞ control is more effective than the traditional LQR/LQG control in terms of the stability and robustness of the control system. The external excitation signal from ground sensors is filtered by a low-pass filter based on SWT and then inputted into the filtered-x RLS adaptive controller. The effectiveness, accuracy, and computational efficiency of the new adaptive control method is demonstrated using a 76-story wind-excited benchmark super high-rise building structure and a 24-story shear-wall building with an active tuned mass damper (ATMD) system on the top floor. Compared with the existing linear quadratic Gaussian control algorithm, the wavelet-hybrid feedback-least mean square algorithm and the robust H∞ control algorithm, the simulation results show that the control effect and robust performance indexes of the structure are increased by 5%–35% and 5%–25%, respectively, using the new control methodology.

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT.

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients’ quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Nonlinear ultrasonic techniques have the benefit of high sensitivity to micro or early-stage defects. Among the various nonlinear techniques, the newly proposed sideband peak count (SPC) technique investigates defect-induced nonlinearity by counting the spectral sidebands from a broadband ultrasonic response in the frequency domain. In this study, SPC analysis is transformed into the time–frequency plane through synchrosqueezed wavelet transform (SWT) for transient nonstationary ultrasonic signals. The proposed new SPC technique was then adopted for in situ porosity monitoring in directed energy deposition (DED)—a typical metal additive manufacturing process. Porosity is one of the most critical defects in DED and has detrimental effects on the mechanical properties and fatigue performance of products. For in situ porosity monitoring, a fully noncontact ultrasonic measurement was achieved with a laser ultrasonic system, and its detectability was improved by laser polishing. Time and frequency windows were properly selected to suppress the effects of wave characteristic variations on the SPC analysis in the SWT domain. The performance of the proposed technique was verified by monitoring porosity in stainless steel 316L samples manufactured in the DED process. The test results demonstrated that the proposed nonlinear technique is much more sensitive to porosity than conventional linear techniques, and hence, is more suitable for in situ porosity monitoring.