Marine SINS Self-Alignment Method in Complex Interference Environment Based on MEEMD Algorithm

In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.

Ballistocardiogram (BCG) could reflect mechanical activity of cardiovascular system instead of ECG. And it is often acquired by sensitive mattress or chair without any constraints and limitations, but it contains many noise because of the impact of body and acquired equipment, those questions make heart rate detection difficult from the original BCG. In the paper, we propose an adaptive method which is used to extract heartbeat intervals (RR), and the method acquire automatically input parameters of Ensemble Empirical Mode Decomposition (EEMD) algorithm, and then decompose BCG signal using EEMD algorithm, and select adaptively decomposition component of BCG signal, whose periodicity is in accordance with the cardiac cycle completely as the target signal. Furthermore we detect the peak points and calculate the heartbeat intervals series using the target signal. In the result, the proposed method is tested using the BCG datasets from 18 subjects, including 8 females and 10 males (age 20–72). Finally, the heart rate from BCG will be compared with ECG, and the results are satisfactory and have a high accuracy.

The noncontact measurement of vital sign signals is useful for medical care, rescuing disaster survivors from ruins and public safety. In this paper, a novel vital sign signal extraction method based on permutation entropy (PE) and ensemble empirical mode decomposition (EEMD) algorithm is proposed. The proposed algorithm analyzes the permutation entropy of radar-received pulses; the range between a human target and ultra-wideband (UWB) radar can be obtained by permutation entropy. Permutation entropy represents the complexity of signals, so we can use PE to select and recombine human life signals that are distributed in the adjacent distance gate. Moreover, EEMD algorithm is adopted to decompose the combined signal into intrinsic mode functions (IMF), and both the respiration and the heartbeat signals are reconstructed by IMF via reaching the energy threshold in the time domain. Experiments are carried out using UWB radar. Compared with traditional algorithms, the proposed algorithm can be used to extract the range and frequency information of human targets efficiently and accurately.

The analytical solution of an approximate (truncated) equation for the vector of a rigid body finite rotation has made it possible to solve the problem of determining the quaternion of orientation of a rigid body for an arbitrary angular velocity and small angle of rotation of a rigid body with the help of quadratures. Proceeding from this solution, the following approach to the construction of the new analytical algorithm for computation of a rigid body orientation with the use of strapdown INS is proposed: 1) By the set components of the angular velocity of a rigid body on the basis of mutually — unambiguous changes of the variables at each time point, a new angular velocity of a rigid body is calculated; 2) Using the new angular velocity and the initial position of a rigid body, with the help of the quadratures we find the exact solution of an approximate linear equation for the vector of a rigid body finite rotation with a zero initial condition; 3) The value of the quaternion orientation of a rigid body (strapdown INS) is determined by the vector of finite rotation. During construction of the algorithm for strapdown INS orientation at each subsequent step the change of the variables takes into account the previous step of the algorithm in such a way that each time the initial value of the vector of finite rotation of a rigid body will be equal to zero. Since the proposed algorithm for the analytical solution of the approximate linear equation for the vector of finite rotation is exact, it has a regular character for all angular motions of a rigid body).

Accurate establishment of initial alignment in shortest duration is vital to strapdown inertial navigation systems (SINS). In self-alignment, initial orientation is established by processing the sensor data itself without any external aid. This work addresses the implementation and performance assessment of self-alignment algorithm to obtain the initial orientation of a ring laser optical sensor based INS in stationary or near stationary condition. Lateral velocity error dynamics is utilized to address the alignment problem based on a Kalman filter approach. Performance is evaluated on lab data acquired on an angular motion simulator with known reference angles. The errors in level angle and azimuth computation are within 3 arcs and 250 arcs, respectively implying a high degree of accuracy. Algorithm also offers a fast convergence with alignment time required being less than 200 s making it ideal for all critical missions where initial self-alignment is a mandate to obtain the initial orientation in static or quasi-static condition.

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS.

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

Although the induction motor (IM) is known for its robustness and low cost of construction, it can happen that the it presents electrical, magnetic or mechanical faults. In this paper, a Hilbert-Huang Transform (HHT) spectral envelope of the stator current signature is proposed for the diagnosis of an IM rotor broken bar (RBB) fault. Firstly, the classical well-known spectral analysis Fast Fourier Transform (FFT) technique is applied to the stator current signal to obtain the harmonic frequency characterizing the RBB fault. Secondly, the HHT technique based on the ensemble empirical mode decomposition (EEMD) algorithm is proposed and applied to the stator current signal to obtain a function called the intrinsic mode function (IMF) containing the frequency that is related to the harmonic frequency characterizing the RBB fault. A comparative study is then carried out to illustrate the HHT technique merits and its superiority with respect to the FFT classical one. To test the effectiveness of the proposed HHT technique and validate the simulation results obtained, several experimental tests are also conducted using a test bench.

Mechanical defect is an important reason for the failure of gas‐insulated switchgear (GIS) equipment. Based on the time‐frequency characteristic vibration signal analysis on five kinds of mechanical defects, a novel intelligent algorithm model combining complementary ensemble empirical mode decomposition (CEEMD) and genetic algorithm improved kernel fuzzy mean clustering (GAKFCM) was proposed to identify the mechanical defect type. First, the mechanical defect platform and detection system were built. Then CEEMD and IMF sensitivity factors were used to analyse the time‐frequency signal of five kinds of vibration defects, and the feature extraction was performed on the processed vibration signals. Finally, the mechanical vibration defect recognition model was established based on the GAKFCM algorithm and its validity was verified. Results show that the developed detection system can detect mechanical vibration signals sensitively. Singular values, frequency band lines and entropy can reflect the energy attenuation and distribution differences for different type of mechanical defect vibration signals. The proposed GAKFCM clustering model combining the above vibration feature parameters can effectively find and diagnose the mechanical defect of GIS equipment. Its recognition accuracy reaches 96.74%, especially for the loose contact seat bolts and poor contact failures of the disconnector.

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

Traction machine is the power source of traction elevator, through fault diagnosis of traction machine can effectively reduce the occurrence of elevator safety accidents. In view of the problems of weak fault characteristics of traction machine and the influence of the vibration signal by the transmitting path, which lead to the poor diagnosis effect, a vibration signal acquisition scheme and fault diagnosis method of elevator traction machine based on improved Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Support Vector Machines (SVM) Algorithm are proposed. Firstly, the vibration signal acquisition scheme of traction machine is designed by finite element simulation of vibration response of traction machine by Abaqus. A fault diagnosis method based on improved CEEMD and SVM Algorithm is proposed to solve the problem of weak fault feature and poor fault diagnosis effect of traction machine. The improved CEEMD method introduces the integrated fault factors to calculate the fault feature correlation degree of each IMF component, and sorts them according to the integrated fault factors from large to small, the Intrinsic Mode Functions (IMF) with larger integrated fault factors is selected and its time-frequency characteristic is calculated to construct the feature vector, and then the tractor fault is identified and classified by SVM. Finally, the effect and recognition rate of this method and the unimproved CEEMD method are compared by fault injection test. Experimental results show that the CEEMD method based on integrated fault factors can effectively extract the fault features of the tractor and improve the recognition rate of the tractor fault diagnosis.

Long-term and seasonal variation in groundwater storage in the North China Plain based on GRACE

Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on the river ecosystem. Numerous studies have been conducted to improve monthly runoff forecast accuracy, of which machine learning methods have been paid much attention due to their unique advantages. In this study, a conjunction model, EEMD-SSA-LSTM for short, which comprises ensemble empirical mode decomposition (EEMD) and sparrow search algorithm (SSA)–based long short-term neural networks (LSTM), has been proposed to improve monthly runoff forecasting. The EEMD-SSA-LSTM model is mainly carried out in three steps. First, the original time series data is decomposed into several sub-sequences. Second, each sub-sequence is simulated by LSTM, of which the hyperparameters are optimized by SSA. Finally, the simulated results for each sub-sequence are summarized as the final results. The data obtained from two reservoirs located in China are used to validate the proposed model performance. Meanwhile, four commonly used statistical evaluation indexes are utilized to evaluate model performance. The results demonstrate that compared to several benchmark models, the proposed model can yield satisfactory forecast results and can be conducive to improving monthly runoff forecast accuracy.

It is very challenging to estimate the accurate heart rate/beat during intense physical activities due to corruption of motion artifacts (MAs). However, it is difficult to reconstruct a clean signal and extract heart rate/beat from contaminated photoplethysmography (PPG) signals. It was also observed that various algorithms have been developed for use in the detection of heart rates during physical activities by reconstructing the contaminated PPG signals to clean PPG signals. Against this backdrop, an overview of the various algorithms was conducted with their results from various works. These results are such that the motion-tolerant adaptive algorithm indicated high agreement and high correlation of more than 0.98 for heart rate (HR) and 0.7 for pulse oxygen saturation (SpO2) extraction between measurements by reference sensors and the algorithm. In addition, the distortion rates were reduced from 52.3% to 3.53%, at frequencies between 1 Hz and 2.5 Hz, when the two-dimensional active noise cancellation algorithm was applied representing daily motion such as walking and jogging. The correlation coefficient between the power spectral densities of the reference and reconstructed heart-rate time series was found to be 0.98, which showed that the spectral filter algorithm for motion artifacts and heart-rate reconstruction (SpaMA) method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities. The experimental result of the single-notch filter and ensemble empirical mode decomposition (NFEEMD) algorithm using the Pearson correlation was 0.992 which illustrated that the NFEEMD algorithm is not only suitable for HR estimation during continuous activities but also for intense physical activities with acceleration. Other algorithms suitable for HR estimation during physical activities include the time–frequency spectrum for the detection of motion artifacts (TifMA) algorithm, novel time-varying spectral filtering algorithm, noise-robust heart-rate estimation algorithm, real-time QRS detection algorithm, and many other algorithms in this regard.