Empirical Wavelet Research Articles

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

BackgroundThe most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders such as conduct disorder (CD). The lack of definitive confirmatory diagnostic tests for ADHD and CD make diagnosis challenging. The distinction between ADHD, ADHD + CD and CD is important as the course and treatment are different. Electrocardiography (ECG) signals may become altered in behavioral disorders due to brain-heart autonomic interactions. We have developed a software tool to categorize ADHD, ADHD + CD and CD automatically on ECG signals. MethodECG signals from participants were decomposed using empirical wavelet transform into various modes, from which entropy features were extracted. Robust ten-fold cross-validation with adaptive synthetic sampling (ADASYN) and z-score normalization were performed at each fold. Analysis of variance (ANOVA) technique was employed to determine the variability within the three classes, and obtained the most discriminatory features. Highly significant entropy features were then fed to classifiers. ResultsOur model yielded the best classification results with the bagged tree classifier: 87.19%, 87.71% and 86.29% for accuracy, sensitivity and specificity, respectively. ConclusionThe proposed expert system can potentially assist mental health professionals in the stratification of the three classes, for appropriate intervention using accessible ECG signals.

Whirling detection in deep hole drilling process based on multivariate synchrosqueezing transform of orthogonal dual-channel vibration signals

In the deep hole drilling process, whirling vibration is easily initiated due to slender drill bar having larger length to diameter with low torsional and bending stiffness, which may increase the roundness error and roughness of the hole, even damage the tool and the wall of hole. Therefore, whirling detection is an important task to improve part quality and productivity in the deep hole drilling process. Due to the background noise and other disturbances, the whirling feature signal contained in the vibration signal is very puny. The whirling detection in deep hole drilling process based on vibration signal has turn into a challenging task. In this paper, a whirling detection method is proposed based on multivariate synchrosqueezing transform (MSST) of orthogonal dual-channel vibration signals. Firstly, the empirical wavelet transform method is used to extract the high multiple frequency components of the spindle rotation frequency. And then, the MSST is introduced to enhance the whirling feature signal contained in the high multiple frequency components. Finally, singular value decomposition (SVD) method is employed to condense the time–frequency spectrum of MSST and the whirling indicator is constructed based on the singular values. The proposed method is validated with BTA deep hole drilling tests, and the results show the superiority of the proposed method and indicate the proposed method has great potential to be used for the online whirling detection.

A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization

The forecasting of ship's roll motion is the key to ensuring the safety of ship surface operations and improving operations efficiency. A new hybrid multi-step forecasting model is proposed in this paper. The proposed model combines three methodologies, including adaptive empirical wavelet transform (EWT), multi-step forecasting under the multi-input multi-output (MIMO) strategy of bidirectional long short-term memory (BiLSTM) model, and hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) hyperparameter optimization. The three sets of ship roll datasets in the South China Sea are selected to verify the performance of the hybrid multi-step prediction model. In the end, the results of the research indicate that: (a) The proposed model has a superior prediction accuracy in multi-step prediction, taking dataset #1 as an example, the root mean square error (RMSE) of the prediction result is 0.0934°, the mean average error (MAE) is 0.0742°, and the mean absolute percentage error (MAPE) is 2.9878%; (b) The proposed hybrid multi-step forecasting model is suitable for different datasets and has strong robustness. Taking the 3-step prediction of dataset #1 to #3 as examples, the RMSEs of the proposed model are 0.0879°, 0.0742°, and 0.0991°, respectively.

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra.

Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting

Short-term wind speed forecasting with high accuracy puts forward a positive influence on implementation of power system dispatch and wind energy utilization. Accordingly, to further promote the prediction accuracy and maintain the prediction stability, an innovation compositive architecture is developed incorporating three modules: data preprocessing, several individual predictors and Volterra multi-model fusion with enhanced multi-objective optimization algorithm. First and foremost, detection and correction of outliers are regarded as a preprocessing technique to process original wind speed sequences, after which empirical wavelet transform (EWT) is further adopted to adaptively decompose the corrected sequences into multiple subsequences. Then, individual predictors including several typical models and the proposed convolutional simplified gated recurrent unit (ConvSGRU), are applied to acquire prediction results by summating the predicted values of each component, which is constructed by PSR to obtain feature matrix as input of the above models. Subsequently, standard deviation (Std.) calculated between the actual values and predicted values of each model is severed as the basis for ensemble modeling, in which multi-model fusion based with smaller Std. on three models are adaptively selected to construct a multi-dimensional matrix as the input of Volterra. In this process, kernel identification parameters of Volterra can be acquired by multi-objective Harris hawks optimization (MOHHO) algorithm with mutation operator (HMOHHO), which can effectively guarantee accuracy and stability of the model simultaneously. Furthermore, four experiments with three datasets collected at different regions are revealed to ascertain forecasting capability of the proposed compositive architecture. The experimental results clarify that: (1) original wind speed sequences based on box-plot are employed to detect and correct, which can greatly decrease the influence of outliers on the sequences while preserving its mainstream trend; (2) the proposed deep network ConvSGRU can give assistance for the forecasting accuracy, thereby contributing to ameliorate performance of the entire compositive architecture; (3) multi-model fusion based on Volterra nonlinear fitting is conducive to further actively compensating for instability of individual models; (4) HMOHHO enables Volterra fitting to receive a series of constructive kernel identification parameters.

An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation

Epilepsy, an incurable brain disorder portrayed by seizures, is the most common neurological disease worldwide. The embryonic detection of epileptic action helps the psychologist for the diagnosis of epileptic seizure and reduces the seizure effect on the patient’s life. Empirical wavelet transform (EWT), and a novel multi-fuse reduced deep convolutional neural network (MF-RDCNN) classifier are integrated to design a computer-aided-diagnosis (CAD) system for meticulous classification of epileptic seizure from electroencephalogram (EEG) signals. The EWT is enforced on recorded EEG signals to extract three distinct band-limited modes (BLMs) and the proposed classifier is used to compute the most informative unsupervised signatures automatically by taking the extracted BLMs as inputs. The experimental results are carried out using three EEG databases provided by Bonn-University, Germany single-channel EEG (dataset-A), Neurology and Sleep Center, New Delhi single-channel EEG (dataset-B), and Boston Children’s Hospital multichannel scalp EEG (dataset-C) to verify the effectiveness of the proposed algorithm. The performance of the proposed method is superior over other prevalent methods and has 100%,99.82% overall mean classification accuracy for two class and three class classification problems respectively using ten-fold cross-validation. Moreover, the proposed method has specificity (SPE) of 99.29%, sensitivity (SEN) of 99.86%, and classification accuracy (ACC) of 99.29% with 0.71% of false positive rate per hour (FPR/h) by taking 40% of data for training, 40% of data for testing, and the remaining 20% of data for validation from the total data incorporated with the database-C. The lesser computational complexity, higher learning speed, short event recognition time, remarkable overall mean classification accuracy, and outstanding overall performance are the major advantages of the proposed EWT-MF-RDCNN method over EWT-RDCNN and RDCNN method for accurate classification of seizure EEG epochs. The digital architecture of the MF-RDCNN classifier is also implemented in a high-speed FPGA processor to develop a CAD system for efficacious diagnosis of epileptic seizure activity online.

A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

Series Arc Fault Detection of Grid-Connected PV System via SVD Denoising and IEWT-TWSVM

Series arc fault (SAF) is one of the most harmful faults during the operation of photovoltaic (PV) systems. It is a challenging task to find SAFs promptly for avoiding the PV fire. Aiming at the SAFs under different operating conditions, a novel detection algorithm by combining the Hankel singular value decomposition (Hankel-SVD) denoising method and the improved empirical wavelet transform–twin support vector machine (IEWT-TWSVM) is proposed. The Hankel-SVD algorithm is used to denoise the dc-bus current, which alleviates the influence of switching frequency and irrelevant background noise effectively. Then, the denoised current is decomposed by the IEWT, and the composite multiscale permutation entropy of each frequency band is input into the TWSVM classifier of the salp swarm optimization for completing the fault detection. The proposed algorithm not only can detect SAFs at various fault locations, but also resist dynamic shading, inverter startup, strong wind, and other interference phenomena. Moreover, the detection performance due to the arc transient process, a long-line fault, a single string array fault, a multiply string array fault, and different sampling rates of this model is verified in this study, and the corresponding results are relatively ideal. Experimental results show that the detection accuracy of the proposed method is as high as 98.10% for the measured data, which is more superior than other methods, such as wavelet decomposition, empirical mode decomposition, statistics methods including mean, standard deviation, and entropy.

Experimental investigation on time‐frequency evolution characteristics of electromagnetic radiation below ULF reflecting the damage performance of coal or rock materials

As a form of energy dissipation, electromagnetic radiation (EMR) is gradually becoming a mainstream method prediction method for damage monitoring of coal and rock. To better improve the prediction accuracy of EMR, it is crucial to investigate time-frequency characteristics of EMR and the influence of coal and rock properties on EMR that occurs in the fracture process. In this paper, the deformation and fracture process of raw coal, shaped coal, and cement specimens under compression are observed, and time-frequency evolution characteristics of EMR below ultralow frequency (ULF) under different loading stages are evaluated. Then, the inherent reasons of time-frequency evolution of EMR reflecting the damage performance are analyzed. Meanwhile, the effect of loading condition and composition and structure of materials on EMR is discussed. Results show that there is obvious EMR below ULF in the deformation and fracture process of coal and rock materials, and the combined denoising method of ensemble empirical mode decomposition (EEMD) and wavelet is suitable for extraction of EMR. Besides, the amplitude is approximately inversely proportional to frequency of electromagnetic signals, and the relationship between pulse count of EMR and damage is related to the homogeneity of coal and rock materials. Moreover, the time-frequency characteristic parameter of EMR, such as amplitude, pulse count, and frequency spectrum, can indirectly reflect the damage evolution process of coal and rock. These results can provide a theoretical basis for remote monitoring of coal and rock dynamic disaster using EMR below ULF.

An improved empirical wavelet transform and sensitive components selecting method for bearing fault

The empirical wavelet transform (EWT) has made outstanding contributions in the field of fault diagnosis. However, when EWT processes complex signals, there may be modal aliasing or meaningless components. In order to solve the spectral segmentation defect of the EWT method and improve its ability to extract bearing fault features, this paper proposes a new algorithm to improve EWT. In order to weaken the influence of extreme points in the complex Fourier spectrum on modal differentiation, the cycle envelope spectral segmentation method is proposed. The maximum envelope fitting method not only reduces the number of useless extreme points, but also highlights each mode. The number of filters is reduced while suppressing the interference of noise on the modal. The components that contain similar information in the result will be merged based on their correlation. Then sensitive IMFs assessing index (SIAI) is proposed to combine correlation and symmetry of the signal, and obtains the characteristics of time domain, frequency domain, and amplitude domain to extract fault information. Locomotive bearing and wind turbine gearbox bearing fault data verified the effectiveness of the method.

Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids

The current study presents a novel approach to the selective identification and localization of voltage fluctuation sources in power grids, considering individual disturbing loads changing their state with a frequency of up to 150Hz. The implementation of the proposed approach in the existing infrastructure of smart metering allows for the identification and localization of the individual sources of disturbances in real time. The proposed approach first performs the estimation of the modulation signal using a carrier signal estimator, which allows for a modulation signal with a frequency greater than the power frequency to be estimated. In the next step, the estimated modulating signal is decomposed into component signals associated with individual sources of voltage fluctuations using an enhanced empirical wavelet transform. In the last step, a statistical evaluation of the propagation of component signals with a comparable fundamental frequency is performed, which allows for the supply point of a particular disturbing load to be determined. The proposed approach is verified in numerical simulation studies using MATLAB/SIMULINK and in experimental studies carried out in a real low-voltage power grid. The research carried out shows that the proposed approach allows for the selective identification and localization of voltage fluctuation sources changing their state with a frequency of up to 150Hz, unlike other methods currently used in practice.

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

A novel computer-aided diagnosis framework for EEG-based identification of neural diseases

Recent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B–PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system.

Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency.

Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.

Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach

The safe and stable operation of wind power systems requires the support of wind speed prediction. To ensure the controllability and stability of smart grid dispatching, a novel hybrid model consisting of data-adaptive decomposition, reinforcement learning ensemble, and improved error correction is established for short-term wind speed forecasting. In decomposition module, empirical wavelet transform algorithm is used to adaptively disassemble and reconstruct the wind speed series. In ensemble module, Q-learning is utilized to integrate gated recurrent unit, bidirectional long short-term memory, and deep belief network. In error correction module, wavelet packet decomposition and outlier-robust extreme learning machine are combined to developing predictable components. An appropriate correction shrinkage rate is used to obtain the best correction effect. Ljung-Box Q-Test is utilized to judge the termination of the error correction iteration. Four real data are utilized to validate model performance in the case study. Experimental results show that: (a) The proposed hybrid model can accurately capture the changes of wind data. Taking 1-step prediction results as an example, the mean absolute errors for site #1, #2, #3, and #4 are 0.0829 m/s, 0.0661 m/s, 0.0906 m/s, and 0.0803 m/s, respectively; (b) Compared with several state-of-the-art models, the proposed model has the best prediction performance.

A nonlinear active noise control algorithm using the FEWT and channel-reduced recursive Chebyshev filter

Active noise control (ANC) has been widely studied to eliminate low-frequency noise. However, efficiently controlling higher-order distortions of nonlinear primary and secondary path is still a major challenge. To address this, this paper proposes a novel nonlinear ANC algorithm based on the fast empirical wavelet transform and recursive Chebyshev nonlinear filter. In this method, the fast empirical wavelet transform based on order-statistic filtering is first applied to decompose non-stationary noise into empirical mode functions (EMFs) which are more stationary and easy to control. Furthermore, a novel third-order Chebyshev nonlinear filter including the non-recursive subsection and recursive one is proposed to individually expand EMFs for compensating higher-order nonlinear distortions from primary path and secondary path. Moreover, the strategy of channel-reduced diagonal structure is designed to optimise the number of expanded channels, and the computational complexity of proposed algorithm is also investigated. Experimental results demonstrate that proposed method performs the highest noise reduction over the conventional algorithms but also has a fast convergence rate.

Frequency Hoyer attention based convolutional neural network for remaining useful life prediction of machinery

Remaining useful life (RUL) prediction based on vibration data is a vital part of bearing prognostic and health management, which can be applied to formulate a suitable maintenance strategy. Recently, attention mechanism has been widely studied and applied in the field of prognostics, which can adaptively enhance the features for RUL prediction and weaken the features interfering with the accurate estimation of the condition. However, insufficient priori information has been provided to the deep learning model via the attention mechanism. The domain knowledge of the special structure and the characteristics of the bearing vibration signal is underutilized in the conventional attention mechanism. An innovative attention based RUL prediction model, called frequency Hoyer attention based convolutional neural network (FHA-CNN), is proposed in this study, which combines a deep learning model and signal processing method organically. The 1D convolutional layer and isometric empirical wavelet transform are developed to extract the latent representation of vibration signals from different scales. The proposed FHA is applied to calculate the weight of the feature map adaptively, in which three types of Hoyer index are adopted to comprehensively evaluate the contribution of each frequency part to the degradation of rolling bearings from the frequency domain perspective. To verify the superiority of the proposed method, two run-to-failure experimental dataset case studies are analysed. The obtained results indicate that the proposed FHA-CNN model exhibits a better performance than conventional deep learning-based RUL prediction methods. In addition, the proposed method concentrates on the special structure of the bearing vibration signal and provides a novel insight into the decision-making processes of deep neural networks.

A Time Synchronization Method Between FBG Deformation Measurement System and Airborne DPOS Based on Waveform Matching

The airborne Distributed Position and Orientation System (DPOS) is an important means for multi-point motion measurement required for earth observation with multi-task sensing loads. The sub-nodes in DPOS rely on the main node to perform transfer alignment for them to get high-precision motion data, which requires accurate deformation data and the relative motion relationship between different nodes. Fiber Bragg Grating (FBG) is one of the main means of deformation measurement at present. However, the time synchronization error between FBG and DPOS directly restricts the accuracy of transfer alignment. For this, a high-precision time synchronization method based on waveform matching is presented. Firstly, the data correlation between FBG and DPOS is analyzed. Then the main time error factors, sampling frequency and time delay are determined. On this basis, the empirical mode decomposition and wavelet analysis are used to extract and match the vibration data from the FBGS and bending angle data calculated by DPOS, thus updating the time-varying sampling frequency and time delay of FBGS, and finally achieving high-precision time synchronization between FBGS and DPOS. Results of ground dynamic experiment show that the standard deviation and mean value of FBG time error is less than 4ms and 2ms respectively, and the deformation displacement error of synchronized FBG data is less than 1%, which provides a prerequisite for high-precision transfer alignment of DPOS.