Showing papers on "Adaptive filter published in 2019"

An Adaptive PR Controller for Synchronizing Grid-Connected Inverters

Abstract: In this paper, an adaptive method for tuning a proportional-resonance controller for synchronization of the grid-connected inverters is presented. In the proposed approach, the grid frequency is obtained by minimizing the error signal using a frequency-locked loop mechanism that consists of a resonant adaptive filter and a perturbation-based extremum seeking algorithm. Simulations and experimental studies are presented to demonstrate performance of the proposed controller in face of the frequency variations of an emulated grid voltage waveform. The results are compared with conventional nonadaptive methods, which indicate that performance characteristics for voltage/frequency tracking and power factor can be achieved based on the IEEE 1547 standard.

Adaptive Ultrasound Beamforming using Deep Learning

Abstract: Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks that adopt the algorithmic structure and constraints of adaptive signal processing techniques can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep~learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.

A Novel UKF-RBF Method Based on Adaptive Noise Factor for Fault Diagnosis in Pumping Unit

Abstract: Fault detection and diagnosis in the pumping unit is a challenging industrial problem for the system that exhibits nonlinearity, coupled parameters, and time-varying noise. This paper proposes a novel combined unscented Kalman filter (UKF) and radial basis function (RBF) method based on an adaptive noise factor for fault diagnosis in the pumping unit. First, to reduce computation and complexity of the diagnosis model, the Fourier descriptor method based on an approximate polygon is presented to extract the features of the indicator diagram. RBF neural network is adopted to establish the fault diagnosis model based on indicator diagram data and production data. In particular, UKF is used to train the weights ( $w_{m,l}$ ), the center ( $c_{m}$ ), and the width ( $b_{m}$ ) of the RBF model. Furthermore, the adaptive noise factor method is proposed to address the adaptive filtering issue in the fault diagnosis model. The proposed method is applied to the pumping unit system, and experimental results show the effectiveness and favorable recognition rate in classifying multiple faults.

Affine-Projection-Like M-Estimate Adaptive Filter for Robust Filtering in Impulse Noise

Abstract: In this brief, an affine-projection-like M-estimate (APLM) algorithm is proposed for robust adaptive filtering. To eliminate the adverse effects of impulsive noise in case of the impulse interference environment on the filter weight updates. The proposed APLM algorithm uses a robust cost function based on M-estimate and is derived by using the unconstrained minimization method. More importantly, the APLM algorithm has lower computational complexity than the M-estimate affine projection algorithm, since the direct or indirect inversion of the input signal matrix does not need to be calculated. In order to further improve the performance of the APLM algorithm, namely convergence speed and steady-state misalignment, the convex combination of the APLM (C-APLM) algorithm is presented. Simulation results verify that the proposed APLM and C-APLM algorithms are effective in system identification and echo cancellation scenarios. It also demonstrates that the C-APLM algorithm improves the filter performance in terms of the convergence speed and the normalized mean squared deviation in the presence of impulse noise.

Robust least mean logarithmic square adaptive filtering algorithms

Abstract: The conventional logarithm cost-based adaptive filters, e.g., the least mean logarithmic square (LMLS) algorithm, cannot combat impulsive noises at the filtering process. To address this issue, we present a novel robust least mean logarithmic square (RLMLS) algorithm by using a generalized logarithmic cost function. The proposed RLMLS algorithm can provide robustness against impulsive noises with the improvement of filtering accuracy. For theoretical analysis, the mean square analysis of RLMLS is provided in terms of the mean square deviation (MSD) and excess mean-square error (EMSE) with a white Gaussian reference. For further performance improvement in different noises, the variable step-size RLMLS (VSSRLMLS) based on the statistics of error is proposed to improve the convergence rate and steady-state mean square error, simultaneously. Analytical results and superiorities of RLMLS and VSSRLMLS in the context of system identification are supported by simulations from the aspects of filtering accuracy and robustness in Gaussian and impulse noises.

Play and Prune: Adaptive Filter Pruning for Deep Model Compression

Abstract: While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters. This results in significant memory requirement as well as computational overheads. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for filter-level pruning of CNNs. Our framework, called Play and Prune (PP), jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning rate, while maintaining the model's predictive performance. Our framework consists of two modules: (1) An adaptive filter pruning (AFP) module, which minimizes the number of filters in the model; and (2) A pruning rate controller (PRC) module, which maximizes the accuracy during pruning. Moreover, unlike most previous approaches, our approach allows directly specifying the desired error tolerance instead of pruning level. Our compressed models can be deployed at run-time, without requiring any special libraries or hardware. Our approach reduces the number of parameters of VGG-16 by an impressive factor of 17.5X, and number of FLOPS by 6.43X, with no loss of accuracy, significantly outperforming other state-of-the-art filter pruning methods.

Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems

Abstract: The recursive least-squares (RLS) adaptive filter is an appealing choice in many system identification problems. The main reason behind its popularity is its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the identification of long length impulse responses, like in echo cancellation. Computationally efficient versions of the RLS algorithm, like those based on the dichotomous coordinate descent (DCD) iterations or QR decomposition techniques, reduce the complexity, but still have to face the challenges related to long length adaptive filters (e.g., convergence/tracking capabilities). In this paper, we focus on a different approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. In other words, a high-dimension system identification problem is reformulated in terms of low-dimension problems, which are combined together. This approach was recently addressed in terms of the Wiener filter, showing appealing features for the identification of low-rank systems, like real-world echo paths. In this paper, besides the development of the RLS algorithm based on this approach, we also propose a variable regularized version of this algorithm (using the DCD method to reduce the complexity), with improved robustness to double-talk. Simulations are performed in the context of echo cancellation and the results indicate the good performance of these algorithms.

Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Abstract: This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

Mean-Square Analysis of Multi-Sampled Multiband-Structured Subband Filtering Algorithm

Abstract: Although the multiband-structured subband adaptive filter (MSAF) and its convergence analysis have been widely studied, the existing analyses are carried out only in the decimated time domain. In this paper, we present a new theoretical mean-square analysis of the multi-sampled MSAF (MS-MSAF) algorithm in the original time domain, whereas the MS-MSAF algorithm extends the original sub-sampled number to a general value. Both the transient and steady-state performances are investigated, which provides a guideline for increasing the convergence rate of the MSAF. Then, the tracking ability is studied in a non-stationary environment. Moreover, the approximative analytical minimum mean-square deviation and optimum step size are derived. Simulation results illustrate the derived performance expressions, which show that there is a relatively good match between theory and practice.

Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures

Abstract: This paper studies multiplicative inflation: the complementary scaling of the state covariance in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and discussed in relation to inflation; nonlinearity is given particular attention as a source of sampling error. In response, the “finite‐size” refinement known as the EnKF‐N is re‐derived via a Gaussian scale mixture, again demonstrating how it yields adaptive inflation. Existing methods for adaptive inflation estimation are reviewed, and several insights are gained from a comparative analysis. One such adaptive inflation method is selected to complement the EnKF‐N to make a hybrid that is suitable for contexts where model error is present and imperfectly parametrized. Benchmarks are obtained from experiments with the two‐scale Lorenz model and its slow‐scale truncation. The proposed hybrid EnKF‐N method of adaptive inflation is found to yield systematic accuracy improvements in comparison with the existing methods, albeit to a moderate degree.

FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal

Abstract: High frequency noise and channel noise are dominant in wireless ECG monitoring systems which can be modeled as white Gaussian noise. Least mean square (LMS) algorithm based adaptive filters are the preferred choice for white Gaussian noise removal, because they require fewer computations and less amount of power consumption. Though LMS algorithm is simple to implement in real time systems, it is necessary to modify the LMS algorithm to reduce the mean square error for improved filtering performance. In this paper, a delayed error normalized LMS (DENLMS) adaptive filter is studied with pipelined architecture to remove the white Gaussian noise from ECG signal. The pipelined VLSI architecture is utilized to boost the operational speed of adaptive filter by reducing the critical path using delay elements. The performance of pipelined DENLMS algorithm is compared with ENLMS and DNLMS algorithms. The pipelined DENLMS filter increases the speed of operation and reduces power consumption at the cost of increase in area due to the presence of latches. Virtex 5 FPGA XC5LVX330 Field programmable gate array has been utilized as target chip to determine the speed, logic utilization and power consumption.

A High-Performance and Energy-Efficient FIR Adaptive Filter Using Approximate Distributed Arithmetic Circuits

Abstract: In this paper, a fixed-point finite impulse response adaptive filter is proposed using approximate distributed arithmetic (DA) circuits. In this design, the radix-8 Booth algorithm is used to reduce the number of partial products in the DA architecture, although no multiplication is explicitly performed. In addition, the partial products are approximately generated by truncating the input data with an error compensation. To further reduce hardware costs, an approximate Wallace tree is considered for the accumulation of partial products. As a result, the delay, area, and power consumption of the proposed design are significantly reduced. The application of system identification using a 48-tap bandpass filter and a 103-tap high-pass filter shows that the approximate design achieves a similar accuracy as its accurate counterpart. Compared with the state-of-the-art adaptive filter using bit-level pruning in the adder tree (referred to as the delayed least mean square (DLMS) design), it has a lower steady-state mean squared error and a smaller normalized misalignment. Synthesis results show that the proposed design attains on average a 55% reduction in energy per operation (EPO) and a $3.2\times $ throughput per area compared with an accurate design. Moreover, the proposed design achieves 45%–61% lower EPO compared with the DLMS design. A saccadic system using the proposed approximate adaptive filter-based cerebellar model achieves a similar retinal slip as using an accurate filter. These results are promising for the large-scale integration of approximate circuits into high-performance and energy-efficient systems for error-resilient applications.

Sea Clutter Cancellation for Passive Radar Sensor Exploiting Multi-Channel Adaptive Filters

Abstract: Sea clutter suppression in passive radar sensor is a challenging problem because the Doppler frequencies of low-velocity sea-surface targets are typically close to the spectrum of the sea clutter. Conventional approaches based on single-channel high-pass filters are effective for clutter suppression only when the clutter is concentrated in low Doppler region. For sea clutter that has a spread spectrum, however, these approaches have to compromise target signal reception. That is, they either form a narrow notch which does not effectively suppress clutter, or generate a broadened null that simultaneously mitigates low-velocity target signals. Therefore, it is desirable to design a filter that forms a notch broad enough to cover the entire clutter spectrum, with the frequency response rising sharply to a high gain outside the clutter band. Toward this end, in this paper, we develop a generalized multi-channel adaptive filter which, by forming multiple sharp notches over a set of discrete frequencies within the clutter spectrum, achieves effective clutter suppression and target signal preservation. We focus on the fast frequency-domain implementation, and the performance analysis in terms of the frequency response, signal energy loss, and computational complexities is also presented. The effectiveness of the proposed approaches is verified using real-data results.

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

Abstract: The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

Widely Linear Complex-Valued Estimated-Input LMS Algorithm for Bias-Compensated Adaptive Filtering With Noisy Measurements

Abstract: In this paper, a novel widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) is first proposed for processing noisy input and output data in the complex domain. The WLC-EIAF consists of two steps: (i) estimation of noise-free input and (ii) update of the weight vector, which is realized by alternating the minimization of an instantaneous perturbation with both input and output data. Based on the WLC-EIAF method and adopting the least mean-square (LMS) scheme, a widely linear complex-valued estimated-input LMS (WLC-EILMS) algorithm is developed. It is able to achieve an unbiased parameter estimation and, thus, outperforms the widely linear complex-valued LMS (WL-CLMS) algorithm in the presence of noisy input and output. In particular, for Gaussian signals, closed-form expressions are derived for its steady-state excess mean-square error performance. Furthermore, the linear complex-valued estimated-input LMS and linear real-valued estimated-input LMS algorithms are presented, which are two simplified versions of the WLC-EILMS for circular and real-valued signals, respectively. Simulation results demonstrate that the proposed methods achieve significantly improved performance in terms of mean-square deviation and mean-square error when compared to the WL-CLMS and CLMS algorithms.

Optimal Complexity Architectures for Pipelined Distributed Arithmetic-Based LMS Adaptive Filter

Abstract: This paper presents three optimal-complexity structures (I, II, III) for pipelined distributed arithmetic (DA) based least-mean-square (LMS) adaptive filter The complexity of proposed structures is reduced by implementing offset-binary-coding (OBC) combinations of input samples on hardware However, some non-OBC outputs are produced, and subsequently eliminated in the error computation during the initial clock cycles For achieving more performance benefits, radix-4 OBC combinations of input samples are implemented with the proposed partial product generators In addition, novel low-complexity implementations for the offset term, weight update block and shift-accumulate unit are also proposed Analysis shows that byte-complexity of proposed structures vary linearly with the order of DA base unit, while their bit-complexity depends on the topology All the structures show significant hardware savings, Structure-I has least critical-path and Structure-II, III offer superior convergence performance Experimental results show that the Structure-I, II and III with 32 nd order filter provide savings 7113%, 7183% and 7308% in area, 6847%, 7001% and 7217% in power, 5174%, 3783% and 4538% in area-per-throughput (APT), 4733%, 3372% and 4319% in power-per-throughput (PPT), 5511%, 5966% and 6420% in slice LUTs, 3533%, 4028% and 4487% in flip-flops over the best existing scheme

Removal of Powerline Interference from ECG Signal using FIR, IIR, DWT and NLMS Adaptive Filter

Abstract: ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.

A Robust Nonlinear RLS Type Adaptive Filter for Second-Order-Intermodulation Distortion Cancellation in FDD LTE and 5G Direct Conversion Transceivers

Abstract: Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stopband isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive-least-squares (RLS) type adaptive filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select filter and dc-notch filter at the output of the proposed adaptive filter ensure that the provided IMD2 replica includes the receiver front-end filtering. A second, robust version of the nonlinear recursive-least-squares (RLS) algorithm is derived which provides numerical stability for highly correlated input signals that arise in, e.g., Long-Term Evolution (LTE)-Advanced intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.

Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation

Abstract: Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber's equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.

Widely Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm

Abstract: The adaptive algorithms applied to distributed networks are usually real-valued diffusion subband adaptive filter algorithms. However, it cannot be used for processing the complex-valued signals. In this paper, a novel augmented complex-valued diffusion normalized subband adaptive filter (D-ACNSAF) algorithm is proposed for distributed estimation over networks. In order to deal with the noncircular complex-valued signals, the D-ACNSAF algorithm uses the widely linear model for a diffusion network. Due to the second-order statistical properties of signal, the D-ACNSAF algorithm can process the circular and non-circular complex-valued signals simultaneously. Moreover, the stability and mean-square steady-state analysis of the proposed algorithm are derived based on the spatial–temporal energy conservation principle. Computer simulation experiments on complex-valued system identification and prediction show that the proposed algorithm has better performance (lower mean-square deviation and faster convergence rate) than diffusion complex least-mean-square and diffusion augmented complex least-mean-square algorithms. And the simulation results are consistent with the analysis results.

A Wavelet-Based Fault Diagnosis Approach for Permanent Magnet Synchronous Motors

Abstract: This paper presents a generic analytical tool to diagnose and classify faults in permanent magnet synchronous motors. The proposed method deploys wavelet transform to extract features for fault diagnosis, which provides a framework for studying nonstationary trends. Analyzing the stator current with wavelet raises a challenge since the energy of fundamental component spreads over different scales in the decomposition. An adaptive filter is used to estimate and remove the fundamental component in stator current. This filter predicts the main harmonic by processing the measured current data in real-time without any speed feedback. The proposed filter is designed in a way that it does not affect or suppress fault related harmonics. The estimation accuracy and convergence rate of this filter is tested and reported by error bounds, which exhibit an acceptable robustness. The validity of the proposed fault diagnosis approach is verified by finite element simulations and experimental results. The effectiveness of this algorithm is tested using two case studies including broken magnet and eccentricity faults. An average accuracy above 96% is obtained using experimental and simulation data. It is proven that the filtering scheme increases the overall accuracy of fault diagnosis.

Adaptive Filter Approach for GPS Multipath Estimation Under Correntropy Criterion in Dynamic Multipath Environment

Abstract: For high-precision positioning and navigation systems, the positioning precision of receiver is jeopardized by multipath interference. Multipath suppression methods based on data processing have drawn much attention recently. The critical step of data processing-based method is to estimate multipath parameters. However, most multipath suppression methods falling into the category of data processing-based methods are limited to Gaussian noises, which means the performance of these methods may be degraded in non-Gaussian noises which are encountered quite often in reality. Besides, only static multipath case is studied in most existing literature, which is not sufficient for potential applications since the occurrence and the disappearance of multipath are always changeable along the movement of receiver. To address these problems, the maximum correntropy criterion(MCC) and the generalized maximum correntropy criterion(GMCC) are integrated into the traditional adaptive multipath estimation(AME) algorithm, named as MCC-AME and GMCC-AME, to handle the dynamic multipath estimation problem in non-Gaussian noises. Furthermore, MCC-AME and GMCC-AME are further improved by adopting forgetting factor, named as RMCC-AME and RGMCC-AME, to improve estimation accuracy and reduce time consumption in a recursive way. The four proposed algorithms also address the problems that the formerly proposed entropy-based multipath estimation algorithms are sensitive to the initial estimation and that the assumption of fixed number of multipath is required. The performance of the four proposed algorithms are analyzed and compared. The analytical results show that GMCC-AME outperforms MCC-AME regarding convergent speed, estimation accuracy and robustness, and RGMCC-AME performs even better than RMCC-AME in the same regard.

Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System

Abstract: In a device-free human detection and tracking system using a received signal strength indicator (RSSI), the change in the RSSI pattern is monitored and analyzed to detect and track the human movement. The variation in measured RSSI signals is one of the major effects leading to significant detection and tracking error. To handle such a research problem, in this paper, we propose adaptive RSSI filtering methods designed by considering both the detection and tracking accuracy and the computational complexity. The novelty of our proposed filtering methods is that, to reduce the computational complexity, the measured RSSI input values are automatically filtered only when they have high variation levels; an appropriate threshold is set and used for the decision. Additionally, to increase the detection and tracking accuracy, the measured RSSI input values with different variation levels are filtered with different filtering levels adaptively. The proposed filtering methods are verified by the experiments, which have been carried out in an indoor environment. Various human movement patterns with different directions and speeds are tested. The experimental results show that, with our test scenarios, the proposed filtering methods can appropriately reduce the RSSI variation. They provide good detection and tracking accuracy, which is measured by the number of times the system can detect and track the human with the correct zone. The computational complexity measured by the number of mathematical operations, used by the proposed methods, is lower than comparative filtering methods.

Frequency Phase Space Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

Abstract: As one of the most important components in rotating machinery, rolling bearings are fragile due to their harsh working environment. The empirical wavelet transform (EWT) method has been used to identify bearing faults by constructing an adaptive filter bank and decomposing the vibration signal into different modes. However, the EWT method separates different parts based on the local extremum of the Fourier spectrum. This segmentation strategy is highly susceptible to other components such as gears, and sometimes the fault characteristics of bearings cannot be detected. To overcome the above drawbacks, frequency phase space empirical wavelet transform (FPSEWT) method is proposed in this paper, which divides the spectrum into several parts and uses the Teager energy distribution as a reference for dividing the Fourier spectrum. Then, the differential search (DS) algorithm is applied to automatically identify the optimal right and left boundaries of the sensitive frequency band. The effectiveness of the proposed approach is verified by both simulated signals and experimental data. The fault feature ratio (FFR) values of sensitive components increased from 8.08% to 18.67%, which indicate that the proposed method can successfully extract fault symptoms of rolling element bearings in the presence of environmental disturbance.