Variational Mode Decomposition Research Articles

Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) for the coming 24 hours in order to predict energy production from photovoltaic (PV) panels and wind turbines (WT) in positive energy buildings. Input data are pre-processed through variational mode decomposition (VMD) for broadband feature extraction, which is then decomposed into smooth modes. The application of the Salp Swarm Algorithm (SSA) aims to optimize VMD parameters to enhance the precision of feature extraction. A thorough data analysis is performed to identify the essential input features. Residual pre-processing between input variables and their decomposed modes further enhances model performance. The stacking algorithm (SA) is used to predict both modes and residuals of the input data. Performance evaluation using metrics such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and normalized mean absolute error (NMAE) indicates a reduction in error rates across measurement scales. For example, under adverse weather conditions, the NRMSE and NMAE for PV power are 2.50% and 1.95%, respectively.

Wavelet transform-based mode decomposition for EEG signals under general anesthesia

Background Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. Methods To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. Results When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. Conclusions The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Closely spaced modes identification of skyscraper buildings via variational mode decomposition with multiple signal classification algorithm and recursive Hilbert transform

Closely spaced modes identification of skyscraper buildings via variational mode decomposition with multiple signal classification algorithm and recursive Hilbert transform

Multi-Frame Vibration MEMS Gyroscope Temperature Compensation Based on Combined GWO-VMD-TCN-LSTM Algorithm

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope’s performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model.

Research on Multi-Scale Signal Processing and Efficient Model Construction Strategies in Lithium-Ion Battery State Prediction

Abstract To tackle the question of limited generalization and inefficiency in predicting state of health (SOH) and state of charge (SOC) in lithium-ion batteries across diverse sequence lengths, a novel hybrid model is developed. This model integrates multivariate variational mode decomposition (MVMD), informer, and long short-term Memory (LSTM) networks. Initially, battery health features are extracted from the charge and discharge curves, which are then validated for their relevance to SOH and SOC via correlation analysis and random forest algorithms. These features undergo multi-scale decomposition using MVMD, thereby encapsulating the intricate dynamics of battery state changes across various time scales. This decomposition enhances the model's adaptability to different sequence lengths, bolstering its generalization capability. Subsequently, the informer model is utilized to identify temporal patterns within the decomposed features. Finally, LSTM exploits its capacity to capture temporal dependencies for further refinement of the predictions. This hybrid strategy yields substantial enhancements in both efficiency and accuracy. Compared to the transformer model, the proposed hybrid model demonstrates a 30% reduction in SOH prediction error and a 22% decrease in SOC prediction error, concurrently slashing training time significantly. Spanning diverse sequence lengths and battery types, demonstrates the model's strong generalization capabilities.

Synchronous decomposition match-reassigning transform and its application in planetary gearbox fault diagnosis

Abstract Under time-varying operating conditions, the instantaneous frequency (IF) of the vibration signal of the planetary gearbox exhibits non-stationary time-varying closely spaced characteristics as well as non-proportional and non-synchronous characteristics, making it difficult for traditional time-frequency analysis (TFA) methods to accurately identify its fault features and obtain accurate time-frequency representations (TFR). To address this challenge, this paper proposes a TFA method based on Synchronous Decomposition Match-Reassigning Transform (SDMRT). Firstly, the successive variational mode decomposition (SVMD) is used to adaptively separate non-synchronous frequency components in the original vibration signal. Then, the chirp rate (CR) describing the signal harmonic structure is used to synchronously match each frequency component, obtaining the TFR corresponding to each component. Next, all TFRs are merged to obtain a complete TFA result. Finally, Finally, the energy spread is re-aggregated to the ridge of the IF using the reassignment principle, resulting in a new frequency estimation operator called the Synchronous Decomposition Match-Reassigning Operator (SDMRO). SDMRT can adaptively separate non-proportional and non-synchronous IFs and achieve precise matching of time-varying closely spaced IFs, thereby completing the TFR of the vibration signal generated by the planetary gearbox. By analyzing simulated signals and measured vibration signals from planetary gearboxes, the method proposed in this paper is significantly superior to other TFA methods, thereby validating the effectiveness and superiority of SDMRT.

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 750g. 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.

State of Charge Balancing Control Strategy for Wind Power Hybrid Energy Storage Based on Successive Variational Mode Decomposition and Multi-Fuzzy Control

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by hybrid energy storage systems (HESSs), this paper proposes a state of charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

ABSTRACTAccurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

Application of the AVMD-AIMCKD method to the enhancement of rolling bearing fault features

Abstract Rolling bearings are prone to failure due to harsh working environments, which can affect production efficiency. Given the background environmental noise interference, it is difficult to extract the fault characteristics of rolling bearings in the early stages of weak fault signals. Based on adaptive VMD combined with adaptive IMCKD (AVMD-AIMCKD), a rolling bearing fault diagnosis method is proposed in this paper. The vibration signal from the rolling element bearing is processed by adaptive variational modal decomposition (AVMD) to extract and select the time-frequency domain signal characteristics. The Adaptive Improved maximum correlated kurtosis deconvolution (AIMCKD) algorithm is used for noise reduction optimization to obtain the best extraction results. Because the traditional method requires input parameter values for VMD and IMCKD, it is not adaptive. The particle swarm optimization algorithm (PSO) is utilized in this study to optimize two variables: the penalty factor and decomposition mode number of variable mode decomposition (VMD). The filter length and shift order of the improved maximum correlated kurtosis deconvolution (IMCKD) are optimized to make it adaptive. The viability of the method is substantiated through simulations, and the efficacy and precision are validated by comparative studies. The experimental results show that the AVMD-AIMCKD method has high accuracy and robustness in rolling bearing fault diagnosis and provides an accurate reference for rolling bearing health monitoring in engineering practice. The AVMD-AIMCKD method effectively extracts the early fault features through adaptive optimization, overcomes the restrictions of traditional methods, and provides a more accurate and reliable tool for bearing fault signal diagnosis.

Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition

AbstractVarious critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)–artificial neural network (ANN) (codename: ABC–ANN). To this end, historical (1979–2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R2: 0.990 and Test R2: 0.989), (Train R2: 0.986 and Test R2: 0.986), (Train R2: 0.991 and Test R2: 0.989) and (Train R2: 0.988 and Test R2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.

Predictive combination model for CH4 separation and CO2 sequestration with CO2 injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling

In the experimental process of separating coal seam gas using the CO2 displacement method, establishing a predictive model for key variables is essential to optimize displacement parameters, increase coal seam gas recovery, and improve CO2 sequestration efficiency. Traditional modeling methods often struggle with the complex nature of industrial data and are susceptible to overfitting due to multicollinearity caused by long-term datasets. This paper presents a hybrid predictive model based on variational mode decomposition (VMD), a spatiotemporal attention mechanism (STA), a bidirectional long short-term memory network (BiLSTM), and an extreme learning machine (ELM). During the offline phase, VMD is used to decompose raw data into intrinsic mode functions (IMFs). The hidden state from the last time step of the STA-BiLSTM is then added to the original data to enrich the features for ELM training. In the online prediction phase, the outputs from the VMD-STA-BiLSTM and VMD-STA-BiLSTM-ELM models are combined using an error reciprocal method to generate the final prediction. The proposed model is validated with experimental datasets from CO2 displacement for coal seam CH4 under various conditions, as well as with N2-ECBM and CO2-ECBM engineering datasets. The results show that the hybrid model surpasses VMD-ELM, STA-BiLSTM-ELM, BiLSTM, STA-BiLSTM, ELM, TCN, and Attention-TCN models in predictive accuracy. Even in multi-step and rolling predictions, the model exhibits minimal impact from cumulative errors, maintaining accurate forecasts with strong generalization and robustness. It effectively captures feature patterns across different datasets and accurately predicts unknown data. The model shows potential for application in diverse scenarios and complex environments, offering reliable support and decision-making for the field application of CO2 displacement in coal seam CH4 separation. It is an effective and promising predictive approach to enhance coal seam gas recovery and CO2 sequestration efficiency.

Fault diagnosis of wind turbine gears based on OCSSA-VMD and WOA-CNN-BiLSTM

Abstract The accuracy of wind turbine gearbox fault diagnosis will be compromised if the fault feature data is not adequately extracted during operation. To enhance fault identification efficiency and mitigate human interference in parameter setting, this paper introduces an optimized mode decomposition algorithm OCSSA-VMD, derived from variational mode decomposition (VMD) and further optimized by osprey-Cauchy-sparrow search algorithm (OCSSA). This algorithm offers two key advantages: (1) automatic optimization of parameters such as the number of modes k and penalty factor α; (2) reduction of feature dimensionality through mean impact value (MIV) algorithm based on minimum envelope entropy principle, resulting in a multi-fault feature vector set from 13 time-domain features in the intrinsic mode function (IMF) optimal component of wind turbine gearbox vibration data. Additionally, a fault diagnosis model WOA-CNN-BiLSTM is proposed based on whale optimization algorithm (WOA) and convolutional neural network-bidirectional long-short-term-memory (CNN-BiLSTM), which demonstrates improved fault classification accuracy to 98.3333% and diagnosis accuracy to 98.3853% under conditions of insufficient data when compared with other models.

Robust load feature extraction based secondary VMD novel short-term load demand forecasting framework

Load forecasting, as a crucial component of the electricity market, plays a significant role in ensuring the secure operation and rational planning of the power grid. However, as the power system becomes increasingly intricate, the demands on load forecasting techniques have escalated. Consequently, to mitigate the errors in short-term load forecasting (STLF) caused by uncertainty factors and to accommodate daily forecasting under abnormal electricity load conditions, this paper proposes a hybrid load forecasting model that combines an improved Secondary Variational Mode Decomposition (SVMD) algorithm with the Informer model. Employing electricity load data from the Panama context, the data is divided into four distinct experimental cases. The outcomes manifest that in contrast to the baseline model, the proposed approach engenders a minimal reduction of 15.08%, 12.95%, and 13.21% in Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. Furthermore, supplementary experimental results demonstrate that the model exhibits strong robustness.

Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.

Data-driven multivariate time series prediction of in-vehicle equipment failure rates

Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.

Research on hydroacoustic signal processing algorithm based on B-spline and Hilbert transform

Purpose This paper aims to solve the problems of baseline drift, susceptibility to abnormal data interference during baseline drift processing, and phase inconsistency in underwater acoustic target detection and signal processing of single microelectromechanical systems (MEMS) vector hydrophone. To this end, this paper proposes a baseline drift removal algorithm based on Huber regression model with B-spline interpolation (H-BS) and a phase compensation algorithm based on the Hilbert transform. Design/methodology/approach First, the Huber regression model is innovatively introduced into the conventional B-spline interpolation (B-spline) to solve the control point vectors more accurately and to improve the anti-interference ability of the abnormal data when the B-spline interpolation fitting removes baseline drift and the baseline drift components in the signals are fitted accurately and removed by the above method. Next, the Hilbert transform is applied to the three-channel output signals of the MEMS vector hydrophone to calculate the instantaneous phase and the phase compensation is performed on the vector X/Y signals with the scalar P signal as the reference. Findings Through simulation experiments, it is found that H-BS proposed in this paper has smaller processing error and better robustness than variational modal decomposition and B-spline, but the H-BS algorithm takes slightly longer than the B-spline. In the actual lake test experiments, the H-BS algorithm can effectively remove the baseline drift component in the original signal and restore the signal waveform, and after the Hilbert transform phase compensation, the direction of arrival estimation accuracy of the signal is improved by 1°∼2°, which realizes high-precision orientation to the acoustic source target. Originality/value In this paper, the Huber regression model is introduced into B-spline interpolation fitting for the first time and applied in the specialized field of hydroacoustic signal baseline drift removal. Meanwhile, the Hilbert transform is applied to phase compensation of hydroacoustic signals. After simulation and practical experiments, these two methods are verified to be effective in processing hydroacoustic signals and perform better than similar methods. This study provides a new research direction for the signal processing of MEMS vector hydrophone, which has important practical engineering application value.

Multivariate variational mode decomposition and 1D residual neural network for subtle feature recognition of rolling bearings

Multivariate variational mode decomposition and 1D residual neural network for subtle feature recognition of rolling bearings

Improved MER algorithm for lost circulation detection using transient pressure waves

In order to solve the difficult problem of locating the leakage layer in the bare eye well section during drilling, this study proposes a transient pressure wave well leakage detection method based on the modified energy ratio (MER) analysis. The method makes clever use of the similarity between the transient pressure waveform and the first arrival of the microseismic signal, and accurately captures the sudden change moments of the pressure wave to obtain the time difference of the signal through the MER method, and combines with the wave velocity of the pressure wave to achieve the precise location of the leakage layer. To address the problem of high noise content in transient pressure wave signals, this study proposes an improved variational modal decomposition (VMD) adaptive denoising method, which effectively removes the noise components and retains the key features of the signals to the maximum extent, and the signal-to-noise ratio of the signal after noise reduction can be up to 15.64. The experimental results show that the leaky layer localisation error of the method ranges from 0.13% to 5.30%. Under the same conditions, the accuracy of the MER localisation method is better than that of the wavelet mode maxima method, the frequency domain method and the short-time averaging/long-time averaging (STA/LTA) method, and the localisation error rate can be as low as 2.11%. The transient pressure wave well leakage detection method based on the improved MER algorithm provides a low-cost, high-precision and efficient solution for well leakage detection during drilling.

A reconstructed PDO history from an ice core isotope record on the central Tibetan Plateau

Ice core oxygen isotope (δ18O) records from low-latitude regions preserve high-resolution climate records in the past, yet the interpretation of these ice core δ18O records is still facing difficulty due to the uncertainty of ice core dating. Here we present a new established δ18O time series from Qiangtang (QT) No. 1 ice core retrieved from the central Tibetan Plateau. Due to the vague seasonal signals in the QT ice core, we investigated the spectral properties of δ18O record with depth and discussed the implications of significant spectral power peaks in the QT ice core. We employed a variational mode decomposition (VMD) analysis for the upper part of the QT ice core to decompose the δ18O depth series in order to separate the El Niño Southern Oscillation (ENSO) mode, a signal strongly preserved in the QT ice core δ18O record. With this approach, we established a time series of 335 years (1677–2011 CE) for the upper 50 m of the QT ice core. Subsequently, we examined the frequency of the new established δ18O time series and detected strong signals of the bidecadal and multidecadal modes of Pacific Decadal Oscillation (PDO). The PDO consists of two modes with periods of approximately 25–35 years and 50–70 years, and we found that the 50–70 years periodicity has persisted since 1700 CE, succeeded by dominance of the 25–75 years periodicity after 1900 CE. Additionally, we analyzed the δ18O series of the QT ice core during the past century and determined that the increasing frequency of El Niño events is an important factor contributing to the increase in recent ice core δ18O.