Improved Threshold Function Research Articles

Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.

The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.

Study on Noise Reduction of Acoustic Emission Signals based on Improved Wavelet Thresholding

The wavelet transform is extensively utilized in signal denoising due to its benefits of reduced entropy, multiple resolutions, and decorrelation. This paper presents an enhanced wavelet threshold denoising algorithm that combines the existing improved threshold function and threshold selection method, building upon the traditional wavelet threshold denoising algorithm. The enhanced threshold function exhibits improved smoothness and reduced coefficient variation; the novel threshold selection approach integrates the Lipschitz properties of the signal and achieves a higher rate of noise signal elimination. The simulation experiments on denoising demonstrate that the enhanced wavelet threshold denoising algorithm enhances the signal-to-noise ratio (SNR) and mean-square error (MSE) by 14.4% and 58.3% respectively, in comparison to the conventional algorithm. Additionally, it outperforms existing algorithms by 8.4% and 36.5%, showcasing its superior denoising capabilities. These findings validate the performance benefits and practical value of the denoising algorithm proposed in this research paper.

Leakage detection method of underground heating pipeline based on improved wavelet threshold function

For urban heating pipeline network, the intelligent and precise detection is an important guarantee for urban heating infrastructure of the safe and low-carbon operation. Heating pipe network was directly buried in the soil and difficult to detect the leakage point due to lack of non-excavation detection method for rapid repair. An improved noise reduction algorithm of the wavelet threshold function coupled with the acoustic method was used for the directly buried pipe leakage detection. Large-scale leakage experimental tests were used to validate the reliability. The influences of temperature, pressure, flowrates and leakage distance on the frequency and time domains were then investigated. Results show that the improved threshold function could well detect the leakage for directly buried hot water heating pipes. When the temperature increases, the leakage frequency domain spreads from 200 - 800 Hz to 50–1500 Hz. The pressure, flow rate, and leak point location could affect the amplitude feature. The accuracy ranges of the improved method in the different location, temperature and pressure and flowrates are separately 0.11–2.36%, 0.11–1.96%,0.11–3.49% and 0.16–1.55%, respectively.

Research on denoising the temperature monitoring signal of a distributed optical fiber downhole cable

Aiming at the noise mixed in the downhole cable skin temperature signal measured by the BOTDR distributed fiber optic sensing temperature measurement system, a new improved wavelet threshold function is proposed, and denoising experiments are completed based on the MATLAB platform for the measured cable skin temperature signal. The denoising results of several threshold function methods are compared and analyzed from the aspects of graphical intuitive comparison as well as quantitative evaluation indicators. The results show that the improved threshold function denoising fiber optic temperature monitoring signal has a higher SNR and lower RMSE, there is no obvious distortion phenomenon, and the denoising performance is significantly improved compared with the traditional threshold function, which has a high application value.

A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding.

Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements. The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10Hz. This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database. The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function. This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis.

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.

Leakage identification for mineral air supply pipeline system based on joint noise reduction and ELM

This work presents a leakage identification framework for the coal mine air supply pipeline system based on joint noise reduction and extreme learning machine (ELM). First, leakage vibration signals from a pipeline system are sampled. Then, vibration signals are decomposed by parameter-optimized variational mode decomposition (POVMD), where the mode number is determined via the energy difference principle, and the separation point of low- and high-frequency modes are identified by mutual information entropy. Next, the wavelet denoising method based on an improved threshold function is implemented on high-frequency modes. Then denoised vibration signals are reconstructed by denoised high-frequency modes and low-frequency modes. Subsequently, multi-scale entropy values are extracted and fed into ELM for training and testing. In the laboratory environment, the ELM model with 132 hidden nodes and the Sigmoid function shows the highest testing accuracy (94.17%), indicating that the proposed method is more practical and effective than other methods.

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

Research on Improved Wavelet Threshold Denoising Method for Non-Contact Force and Magnetic Signals

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.

Study on Optical Fiber Gas-Holdup Meter Signal Denoising Using Improved Threshold Wavelet Transform

The optical fiber probe gas-holdup meter is a new logging tool, which can be used to measure the gas-holdup of oil-gas-water three-phase flow during production logging. In this paper, the wavelet transform is used to denoise the measured gas-holdup signal of the optical fiber probe. For the wavelet transform threshold de-noising method, the existing hard and soft threshold functions have certain defects. So we propose a wavelet transform denoising method with an improved threshold function, and use this method to denoise the measured gas-holdup signal of the optical fiber probe. The signal-to-noise ratio, root mean square error, smoothness, and correlation coefficient are used as the evaluation indexes of signal denoising effect. The improved threshold function is between the hard threshold function and the soft threshold function. It has the advantages of both hard and soft threshold functions, can effectively overcome the discontinuity of the hard threshold function and the constant deviation of the soft threshold function, and improves the denoising efficiency of the gas-holdup signal. The proposed method is applied to the synthetic data and the real gas-holdup signal of the optical fiber probe respectively. Compared with the traditional soft and hard threshold functions, the experiment results show that the improved threshold wavelet denoising method is feasible. And it is effective in the gas-holdup signal denoising, improving the signal-to-noise ratio, reducing root mean square error, and increasing the correlation coefficient. The proposed method preserves well the original features of the measured gas-holdup signal, which provides a reference for the signal processing and dynamic monitoring analysis of the subsequent oil-well downhole development.

Design of pulse laser high-precision ranging algorithm under low signal-to-noise ratio

Abstract Because the traditional pulse laser high-precision ranging method has the problems of low ranging accuracy and long ranging time, a pulse laser high-precision ranging algorithm under low signal-to-noise ratio is proposed. Based on the analysis of the basic principle of pulse laser ranging, the pulse laser signal under low signal-to-noise ratio is obtained by modeling the pulse laser echo analog signal waveform. The wavelet denoising method of improved threshold function is used to denoise the pulse laser signal under low signal-to-noise ratio. According to the processing results, the pulse laser high-precision ranging is carried out through sinusoidal amplitude time conversion. The simulation results show that the proposed algorithm has high precision and short ranging time.

Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization

Noise significantly reduces the detection accuracy of transient power quality disturbances. It is critical to denoise the disturbance. The purpose of this research is to present an improved wavelet threshold denoising method and an adaptive parameter selection strategy based on energy optimization to address the issue of unclear parameter values in existing improved wavelet threshold methods. To begin, we introduce the peak-to-sum ratio and combine it with an adaptive correction factor to modify the general threshold. After calculating the energy of each layer of wavelet coefficient, the scale with the lowest energy is chosen as the optimal critical scale, and the correction factor is adaptively adjusted according to the critical scale. Following that, an improved threshold function with a variable factor is proposed, with the variable factor being controlled by the critical scale in order to adapt to different disturbance types’ denoising. The simulation results show that the proposed method outperforms existing methods for denoising various types of power quality disturbance signals, significantly improving SNR and minimizing MSE, while retaining critical information during disturbance mutation. Meanwhile, the effective location of the denoised signal based on the proposed method is realized by singular value decomposition. The minimum location error is 0%, and the maximum is three disturbance points.

Research on Construction Method of Wavelet Telemetry Data with Improved Threshold

Abstract In order to strengthen the applicability of data denoising algorithm, this thesis study the common telemetry data denoising algorithm based on the data of engine speed, flight space speed, cabin temperature and humidity, and establishes the evaluation model of error square sum and curve similarity to evaluate the denoising performance. Experiments show that the polynomial fitting has the greatest denoising error and slow convergence speed. The five-point cubic smoothing has the smallest overall denoising error, the median filtering algorithm can change the effect of smoothing effect by adjust it's moothing window, but ignores the authenticity of data. Therefore, the above three data denoising algorithms do not meet the requirements of telemetry data processing. In this thesis, an improved threshold function is proposed which effectively improves the data jump and excessive smoothing and reduce the denoising accuracy compared with the traditional thresholding function in order to makes the measured value closer to the true value. The algorithm is applied to the noise processing of four kinds of telemetry data, the results show that the denoising accuracy is improved significantly compared with the other three algorithms, which makes the measured value closer to the true value to reflect the changing trend of the original measurement data more truthfully, and the curve similarity is improved significantly, which are all above 80%.

Research on De-noising of Downhole Engineering Parameters by Wavelet based on Improved Threshold Function

The measurement of downhole engineering parameters is greatly disturbed by the working environment. Effective de-noising methods are required for processing logging-while-drilling (LWD) acquisition signals, in order to obtain downhole engineering parameters accurately and effectively. In this paper, a new de-noising method for measuring downhole engineering parameters was presented, based on a feedback method and a wavelet transform threshold function. Firstly, in view of the mutability and density of downhole engineering data, an improved wavelet threshold function was proposed to de-noise the signal, so as to overcome the shortcomings of data oscillation and deviation caused by the traditional threshold function. Secondly, due to the unknown true value, traditional single denoising effect evaluation cannot meet the requirements of quality evaluation very well. So the root mean square error (RMSE), signal-to-noise ratio (SNR), smoothness (R) and fusion indexs (F) are used as the evaluation parameters of the de-noising effect, which can determine the optimal wavelet decomposition scale and the best wavelet basis. Finally, the proposed method was verified based on the measured downhole data. The experimental results showed that the improved wavelet de-noising method could reduce all kinds of interferences in the LWD signal, providing reliable measurement for analyzing the working status of the drilling bit.

EFFECT OF WAVELET DE-NOISING ON THE CLASSIFICATION OF PIG BEHAVIOUR

To efficiently eliminate the noise generated by the triaxial accelerometer when collecting pigs’ behavioural data, this paper adopted SNR and MSE as the indexes to evaluate the de-noising effect of pigs’ acceleration signal under various combinations of wavelet basis, decomposition layer, threshold rule and threshold function. Based on the optimal wavelet parameter combinations, the de-noised data were divided into a training dataset and test dataset to conduct a 3-fold cross validation. The results showed that Db4 wavelet can achieve a satisfactory de-noising effect when used as a wavelet basis for 8 layers wavelet decomposition based on Rigrsure threshold rules and the new improved threshold function. As a result, compared with traditional wavelet hard threshold de-noising, soft threshold de-noising and EMD de-noising method, the improved threshold function improved the stability of signal filtering, which was shown to be more practical, effective and feasible. As such, wavelet de-noising was found to significantly improve the classification accuracy of all four behaviour classes (lying, standing, walking and exploring) considered for this study, and the overall major mean accuracy was improved from 0.680 to 0.826.

Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency

The machining condition monitoring is an important task to improve product quality and avoid economic losses in deep-hole drilling. Out-of-roundness-tolerance is one of major defects in deep-hole drilling process. In this work, the spindle vibration signals generated in deep-hole drilling process are studied to seek the characteristics which can reveal the roundness error change. Considering the influence of background noise on monitoring effect and the sensitivity of high multiple frequency components of spindle vibration signal towards the roundness error change, an improved empirical wavelet de-noising method is proposed to remove the noise of the spindle vibration signal. The high multiple frequency components of the rotation frequency are extracted from the de-noised spindle vibration signal. Firstly, the spindle vibration signal is decomposed by empirical wavelet transform into a set of wavelet coefficients, and then an improved thresholding function is proposed to process the Fourier spectrum of wavelet coefficients for removing the noise from the wavelet coefficients. Secondly, the wavelet coefficients are reconstructed based on the de-noised wavelet coefficients, and the wavelet coefficients which contain high multiple components of rotation frequency are selected. Finally, the energy entropy of the selected wavelet coefficients is calculated as a roundness error monitoring indicator to detect the roundness error of deep hole. The proposed method is verified with well-designed deep-hole drilling tests. The test results show that the monitoring indicator can effectively reflect the roundness error change in deep-hole drilling process.

Application of Improved Wavelet Threshold in Denoising of ECG Signals

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.

An Ultrasonic Signal Denoising Method for EMU Wheel Trackside Fault Diagnosis System Based on Improved Threshold Function

In the safety protection system of the railway electric multiple unit (EMU), the safety of the running part is extremely important. The daily detection of the internal hazard defects of the wheels in the running parts relies on a professional trackside fault online diagnosis system based on the ultrasonic sensor probe array data. However, the on-line ultrasonic diagnosis of EMU wheels is usually accompanied by various interference noises. The defect echo signals collected by the sensor probe array are weak and are easily submerged by noise, which makes it impossible to perform effective defect identification. This paper proposes an improved threshold function to overcome the discontinuous shortcomings of the classical wavelet soft threshold function and hard threshold function in view of the non-stationary characteristics of the ultrasonic detection signal of the EMU wheels. This paper proposes a sine-type threshold processing function. It is characterized by adopting gradual compression processing to denoise the echo signal of the ultrasonic sensor probe array. In order to verify the validity, the continuity at the threshold is observed through the linear space vector signal, and the algorithm is simulated and tested through the three-dimensional Gaussian echo mathematical model of the ultrasonic signal and the measured ultrasonic envelope signal. Experimental results show that the improved threshold function can suppress the noise in the ultrasonic echo data, improve the signal-to-noise ratio, and retain the waveform characteristics of the defect signal, which is conducive to defect recognition.

An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.