Optimal Filter Parameters Research Articles

Controlling mode-coupling instability in friction-induced vibration by acceleration feedback

Self-excited friction-induced oscillation, such as brake squeal, railway wheel squeal, chattering in machine tools, etc., is harmful in many mechanical systems. In the present study, the efficacy of acceleration feedback control with a second order filter is theoretically studied to mitigate friction-induced mode-coupling instability. Two different control strategies based on acceleration feedback are employed to control friction-induced mode-coupling instability of a two degrees-of-freedom linear system. Linear stability analysis near the equilibrium point of the system shows the efficacy of control laws to stabilize the system. The optimum filter parameters are determined by the pole crossover optimization method to attenuate the transient response as well as to achieve greater relative stability of the system. The Robustness study confirms the ability of the controller to stabilize the system in the presence of uncertainties in system parameters. Analytical results match closely with simulation results. Finally, nonlinearity in the form of stiffness is incorporated into the system for which the dynamics of the system show the presence of subcritical, supercritical Hopf bifurcation, and cyclic fold bifurcation. In the presence of nonlinearity, the feedback control is also able to reduce the range of instability of static equilibrium as well as the amplitude of the self-excited oscillation. The method of averaging is used to determine the stability and amplitude of the limit cycle. For the nonlinear system, the bifurcation diagram is constructed in AUTO and validated with results obtained from MATLAB SIMULINK and the method of averaging.

Optimally filtering and matching processing for regional upstrokes to improve ultrasound transit time-based local PWV estimation

Background and objectivePulse wave velocity (PWV) is an important index for quantifying the elasticity of artery. Local PWV estimates based on ultrasonic transit time (TT) methods, however, are affected by the reflected waves and ultrasonic noise, biasing the spatiotemporal propagation of the time fiduciary point (TFP) positioning in the distension waveforms. In this study, an optimally filtering and matching processing for regional upstrokes is proposed to improve the ultrasound TT-based local PWV estimation. Method(i) Smooth the pulse waves (PWs) using the Savitzky–Golay filter with one set of randomly combined parameters. (ii) An arbitrary region at the first beam upstroke of the smoothed PWs is selected as the curve template, and then matched with the upstrokes of other PWs by calculating the sum of square differences (SSD) between the template and matching regions to find its similar regions. (iii) Update the filter parameters and the template using the moth-flame optimization (MFO) feedback for computing the new SSD value. When the new SSD value is smaller than the historical one, the later will be replaced. (iv) Repeat the above steps until the MFO algorithm converges to the minimum SSD value. (v) Output the optimal filter parameters and the locations of regional curves corresponding to the minimum SSD value. Then the time delay of the PWs propagation can be detected by using the starting points of the regional curves as the TFPs. ResultsWe conducted performance comparison with the advanced TT method through both simulation and clinical experiments. The results demonstrate that the proposed work observes considerable reductions on both the normalized root mean square error ± the standard deviation (from 6.73 ± 2.27% to 1.57 ± 0.72%) and the coefficient of variation (from 13.39% to 8.87%). ConclusionsThe results of this study support that the proposed method may facilitate the early diagnosis and prevention of local arterial stiffness .

Handiski simulator performance under PSO-based washout and control parameters optimization

The transfer of advanced technology to the person with a disability who wishes to practice a sporting activity is gaining momentum in the science/engineering world. This paper seeks to approve more comfort and sensations for people with paraplegia on a motion simulation platform during a ski operation. The Motion Cueing Algorithm (MCA) has proven itself for sensation reproduction, which we propose to improve by integrating the physical limits of our 8-DoF mechatronics platform. An extended classical MCA is proposed to respond to the significant lack of restored sensation in the intermediate frequency range. A Particle Swarm Optimization (PSO) is constructed to perform optimal washout filter parameters and control law parameters. Consequently, the reproduced skier trajectory stability is obtained. The results show that the proposed algorithm will overcome the physical limitation problem in the Handiski simulator, improve the realism of movement sensation, and reduce the false cues to enhance dynamic fidelity.

Comparison of the main aspects of modern approaches to the development of surface acoustic wave filters: model of the coupling of mode and the finite element method

This article discusses the main issues of designing filters based on surface acoustic waves. A type of band-pass filter based on longitudinal resonant modes is presented. The features of the calculation based on two approaches are considered: the coupled mode model and the finite element method. Practical recommendations are proposed for reducing the time of filter calculation in numerical simulation. The results of calculating and measuring the transmission coefficient of a filter on a leaky surface acoustic waves on a 36 YX-cut lithium tantalate substrate are presented and compared. The main aspects and directions in which the considered modeling methods can be compared are highlighted and analyzed. It is shown that the use of different modeling approaches increases the development efficiency, and fast analytical models are required for the synthesis and optimization of filter parameters.

Properties on Bearing Fault Diagnosis of Water Injection Motor

Water injection motor in western Daqing Oilfield is the key equipment of water injection to the oil field. Due to the great pressure of water injection to the oil field, the water injection motor is often overloaded and runs at high speed, so that the rolling bearing of the motor is worn, deformed and other faults. In order to detect the small faults that are not easy to detect in the early stage of the motor bearing, this paper proposes a method combining variational mode decomposition (VMD) and fast spectral kurtosis. The optimal number of modes is determined by correlation and energy ratio. The fast spectral kurtosis operation is carried out on the modes with large kurtosis. The fault signal is enveloped and decomposed by the optimal filter parameters. The experimental results show that the algorithm can successfully identify the rolling bearing fault of water injection motor and determine the fault location.

Rectangular Orthogonal Digital Filter Banks Based on Extended Gaussian Functions

Rectangular orthogonal digital filter banks (ODFBs) based on square-root-raised-cosine (SRRC) functions are widely utilised to realise flexible and elastic multi-channel aggregations for fixed and mobile networks. However, long digital filter lengths are required to minimize digital filtering-associated signal distortions. In this paper, based on the extended Gaussian function (EGF), a novel rectangular ODFB with excellent robustness against the short digital filter length-induced truncation effect is proposed. Optimum digital filter parameters of the EGF-based ODFBs are identified and verified in multi-channel hybrid OFDM-digital filter multiple access (DFMA) PONs based on intensity modulation and direct detection (IMDD). By making use of the identified optimum digital filter parameters, extensive comparisons of digital filter characteristics and corresponding multi-channel upstream PON performances are made between the EGF-based ODFBs and the SRRC-based ODFBs. It is shown that to achieve a similar aggregated upstream signal transmission capacity, the EGF-based ODFB reduces the digital filter DSP complexity by a factor of 4. For a digital filter length as short as 8, in comparison with the SRRC-based ODFB, the EGF-based ODFB introduces >1.5 dB (>0.8 dB) improvements in upstream receiver sensitivity for 5-bits (8-bits) DACs/ADCs, increases the aggregated upstream signal transmission capacity by >5.5%, enlarges the ONU launch power dynamic range by >2.5 dB and improves the frequency offset tolerance by a factor of >1.5. In addition, the EGF-based ODFB also enhances upstream performance robustness against ONU symbol timing offsets.

Adaptive event‐triggered fault detection for nonlinear networked systems in finite‐frequency domain under stochastic cyber‐attacks

SummaryThis article investigates the fault detection problem in finite‐frequency domain for a class of nonlinear networked systems under stochastic cyber‐attacks. A novel adaptive event‐triggered scheme is introduced to mitigate the transmission burden of the network. A unified measurement model is proposed to take the randomly occurring cyber‐attacks and the transmission delays into account simultaneously. Under the consideration of fault sensitivity and disturbance robustness, the addressed fault detection problem is converted into an auxiliary filtering problem by properly augmenting the states of the original system and the fault detection filter. Intensive stochastic analysis is carried out to obtain sufficient conditions for the existence of the desired fault detection filter, and the corresponding optimal filter parameters can be easily derived by solving a convex optimization problem. Finally, an illustrative example is presented to show the effectiveness and applicability of the proposed method.

Event-triggered fault detection for T-S fuzzy systems with local nonlinear models in finite-frequency domain

This article investigates the issue of event-triggered fault detection (FD) filter design for T-S fuzzy systems with local nonlinear models. A novel FD filter subject to the event triggering transmission mechanism is designed in finite-frequency domain. Then, a novel lemma, in which the nonlinear part and the event triggering mechanism are dealt appropriately, is presented to capture the sensitivity and robustness performances. In addition, the slack matrices are utilised to derive optimal filter parameters by solving a convex optimisation problem. The less conservative FD method can get better detection performances than those entire-frequency methods. Finally, an example is introduced to verify the new results.

Deep Learning for Denoising: An Attempt to Recover the Effective Magnetic Resonance Sounding Signal in the Presence of High Level Noise

Magnetic resonance sounding (MRS) measurements commonly suffer from a notably low signal-to-noise ratio (SNR). In recent years, many denoising methods have been developed, which have demonstrated the useful capability to improve the SNR. However, when MRS measurements are implemented on the sites with high noise levels, i.e., the human living environment, the conventional methods are helpless to recover the effective MRS signal, which is submerged in extensive environmental noise. In addition, the conventional methods that depend on signal models and the corresponding prior assumptions commonly rely on manual experience, which brings obstacles to the automation and efficiency of signal processing. To solve the above problems, we attempt to apply an intelligent denoising framework with a novel neural network as the basic tool for deep learning, called Dn-ResUnet in this article. The network extracts the features of the MRS signal through the encoder and decoder layers of the Dn-ResUnet structure, in which residual learning is adopted to accelerate the training process and improve denoising performance. Once the training is completed, the deep learning realizes adaptive denoising with no need for 1) prior assumptions of the MRS signal and noise; 2) optimal filter parameter tuning; or 3) expensive time cost. The comparison experiments demonstrate that the Dn-ResUnet model provides superior noise cancellation performance, especially it can replace the conventional methods to recover the effective MRS signals in noise levels down to an SNR of −30 dB. In addition, the noise attenuation tests are performed on synthetic and real data. The results show that the framework of deep learning yields a convincing performance in MRS signal denoising.

Realization of fractional band pass filter on reconfigurable analog device

Abstract This paper presents a realization of fractional-order Band pass-filter (FOBF) based on the concepts of fractional order inductors and fractional order capacitors. The FOBF is designed and implemented using both simulation and hardware approaches. The proposed filter order is considered up to second order or less with any real positive number. One of the cases is considered when α ≤ 1 and β ≥ 1. In the second case, the filter is designed when β ≤ 1 and α ≥ 1. In order to calculate the optimal filter parameters, the modified Particle Swarm Optimization (mPSO) algorithm has been utilized for coefficient tuning. Also, a generalized approach to design any second order FOBF is discussed in this work. The realization and performance assessment have been carried out in simulation environment as well as in lab experiment with field programmable analog array (FPAA) development board. The experimental results indicate the value of efforts to realize the fractional filter.

A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation.

In Kalman filter design, the filter algorithm and prediction model design are the most discussed topics in research. Another fundamental but less investigated issue is the careful selection of measurands and their contribution to the estimation problem. This is often done purely on the basis of empirical values or by experiments. This paper presents a novel holistic method to design and assess Kalman filters in an automated way and to perform their analysis based on quantifiable parameters. The optimal filter parameters are computed with the help of a nonlinear optimization algorithm. To determine and analyze an optimal filter design, two novel quantitative nonlinear observability measures are presented along with a method to quantify the dominance contribution of a measurand to an estimate. As a result, different filter configurations can be specifically investigated and compared with respect to the selection of measurands and their influence on the estimation. An unscented Kalman filter algorithm is used to demonstrate the method’s capabilities to design and analyze the estimation problem parameters. For this purpose, an example of a vehicle state estimation with a focus on the tire-road friction coefficient is used, which represents a challenging problem for classical analysis and filter parameterization.

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System.

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

Synthetical Optimal Design for Passive-Damped LCL Filters in Islanded AC Microgrid

In AC microgrid systems, technical issues resulting from the power control effect are the power control method efficiency and the filter resonance since voltage-frequency interference and filtering resonance may cause significant control input reduction and oscillation. A simple and robust solution is to regulate active power flow by adding an optimal passive damping resistor. The optimal LCL filter parameterization process in this paper provides an innovative approach to obtain the optimal quality factor and the optimal damping resistor. The amplitude responses of resonant frequency and high frequency are involved in the cost function. Simulations of various cases are conducted in MATLAB/Simulink. The key advantages of this procedure include flattened admittance peak, weakened high frequency decay, and less damping loss. Through the method in this paper, the optimal passive-damped filter parameters can be calculated in an efficient manner, while the power can be regulated and tracked in a simplified way, and the performance of the control strategy is improved.

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Efficiency of adaptive filter with envelope tracking algorithm when receiving signals with multi-position PSK against the background of non-fluctuation interferences

The energy and spectral advantages of signals with phase-shift keying predetermined their widespread use in modern digital navigation, communications and television systems. The use of this type of signal in a multi-position format with an increased number of possible phase states allows increasing the information capacity of the channel signal, and consequently the throughput rate of radio channels. The presence of non-fluctuation interference in such radio channels greatly reduces the noise immunity of information reception. A well-known way to deal with such interference is to use adaptive non-recursive filters in the receiver. The article evaluates the effectiveness of using such a filter with an algorithm for setting weight coefficients that controls the constant envelope of a useful signal when receiving signals with multi-position phase shift keying against the background of noise and non-fluctuation interference. Two types of such interference are considered – harmonic and relayed. Using computer simulation, the optimal filter parameters (adaptation coefficient and its length) are determined; the effect of the filter on the noise immunity of the quadrature signal receiver with multi-position phase shift keying for various combinations of interference and their intensity is estimated. It is shown that such an adaptive filter can successfully cope with the most dangerous sighting harmonic interference.

Optimal Hermitian Wavelet Filtering and Envelope Spectrum Based Bearing Defect Diagnosis

Local faults in rolling bearings often give rise to periodic impulses in vibration signal. The periodic impulses can be extracted by resonance demodulation technique. Therefore, the resonance demodulation technique is a popular technique of rolling bearing defect recognition and diagnosis. However, the selection of optimal band-pass filter parameter is usually more difficult. The proposed method is the union of the continuous Hermitian wavelet transform as time frequency tool and the spectral kurtosis as transient impulse indicator. Firstly, continuous Hermitian wavelet transform is utilized to process the vibration signal and the wavelet amplitude and phase diagram are obtained. Secondly, the spectral kurtosis is calculated to select the best band-pass filter parameters. The proposed procedure and spectral kurtosis are used to optimize the parameters of continuous Hermitian wavelet for bearing defect detection. The bearing failure test results show that the proposed method can effectively extract the transient features and diagnose the bearing fault.

Combination-Chord Measurement of Rail Corrugation Using Triple-Line Structured-Light Vision: Rectification and Optimization

Combination-chord model (CCM), specially designed to broaden the range of rail corrugation measurement, still faces two troubles including the measuring point deviation and the inverse filter parameters optimization. In this paper, a triple-line structured-light vision (SLV) based CCM is introduced to address these issues. Two solutions respectively for rectification of the measuring points deviation and optimization of the inverse filter parameters are presented. The former, based on the horizontal spacing constraint, detects the exact measuring point from a true rail profile which is reconstructed and rectified by the triple-line SLV. The latter, based on the minimum cumulative recovery error, defines the optimized numbers of frequency sampling and filter order, together with a splicing wavelength. Employing these optimized parameters, our CCM yields the overall amplitude-frequency response being nearly one whether for longwave corrugation or for shortwave corrugation. Both of the proposed solutions aim at enhancing the ordinary CCM in terms of the validity for rail corrugation measurement. Experimental assessments verify the availability and repeatability of our scheme.

Space‐time matched filter design for interference suppression in coherent frequency diverse array

By transmitting a single frequency-shifted waveform, coherent frequency diverse array (FDA) can provide a simple way to realize full spatial coverages with stable gains. Owing to the range-angle dependency in coherent FDA, the authors implement a two-dimensional angle-time matched filter to perform equivalent transmit beamforming and matched filtering simultaneously. However, such filter structures merely control main-lobes of equivalent transmit beams towards target directions. It fails to form nulls at interference directions. Moreover, traditional adaptive beamforming by designing adaptive weight vectors are no longer applicable. Additionally, they find that the range resolution scales linearly with the element number. To tackle these issues, a space-time matched filter (STMF) in combination with the hybrid coding technique is proposed. Aiming at mitigating interferences, the STMF is designed with a formulation of quadratically constrained quadratic programing. By relaxation of quadratic constraints, the hard non-convex problem can be turned into the second-order cone programing to obtain optimal filter parameters. Furthermore, the hybrid coding technique is devised to jointly improve the range resolution. Numerical experiments of both two-dimensional range-angle outputs and one-dimensional range profiles via filtering are provided, which demonstrate that the STMF with hybrid coding can effectively suppress sidelobe interferences with a range resolution enhancement.

Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis

Objectives In order to solve the problems of redundant information and low fault feature extraction rate in the vibration signal of marine shafting, a fault feature extraction method based on empirical wavelet transform-spectral kurtosis (EWT-SK) is proposed. Methods First, the original signal is processed by empirical wavelet transform (EWT) to eliminate the excessive vibration components. This method solves the inherent defects of empirical mode decomposition (EMD) such as the endpoint effect and mode aliasing. Second, the modal function is reconstructed based on kurtosis and correlation coefficient, highlighting fault information and improving the signal-to-noise ratio. Finally, the optimal bandpass filter parameters are obtained by spectral kurtosis and used to design filters, then the filtered signal envelope is demodulated to realize fault diagnosis. Results According to the results of case analysis and verification, in the aspect of signal decomposition, EWT has higher stability and efficiency in feature extraction, enabling it to ensure the integrity of shafting fault information. In the aspect of the denoising effect, after using the EWT-SK method, the kurtosis value of the fault signal is 4.761 6, the number of the correlation coefficient is 0.708 8 and the signal-to-noise ratio is 3.762 4, which is better than EMD and variational mode decomposition (VMD). Conclusions The EWT-SK method has good feature extraction ability and noise suppression ability, making it suitable for the fault diagnosis of marine shafting.

Design of fractional delay repetitive control with a dead-beat compensator for a grid-tied converter under distorted grid voltage conditions

The paper presents the design of a current control system of a grid-tied converter which provides high dynamic in transients states and symmetrical sinusoidal currents under distorted and asymmetrical grid voltage conditions. The current control loop contains a dead-beat compensator and a plug-in fractional delay repetitive controller. The whole control system is designed with regard to signal processing delay present in the control loop. The tuning procedure of the repetitive control uses particle swarm optimization to determine the optimal gain, phase lead and filter parameter. The resulting control system with optimal parameters is applied on the digital signal controller and verified experimentally on the laboratory test bench, which is the main contribution of the paper.