Improved Matching Pursuit Algorithm Research Articles

Integrated thrust ripple identification and compensation for linear servo system using an MP algorithm

Generally there are two methods for the thrust ripple modeling: one is to identify the eigenvalues and the model parameters of thrust ripple sequentially under rigorous operating conditions; another is to treat thrust ripple as a general and inaccurate disturbance. To get rid of the constraints and further enhance the tracking accuracy of the linear servo system, a novel integrated identification and compensation scheme using an improved matching pursuit algorithm is proposed in this paper. First, the dynamics of the linear motor is formulated, and the cause of thrust ripple is analyzed. Then, a time–frequency atomic library of thrust ripple is constructed with the consideration of the frequency characteristics of position command, and meanwhile, the two optimal atoms are obtained successively by maximizing the inner product for the thrust ripple reconstruction. Based on the above, the eigenvalues and model parameters of thrust ripple can be simultaneously identified in an online manner. Finally, the feedforward compensation component is devised by the identification results to suppress thrust ripple. In order to verify the superiority of the proposed scheme, simulations and experiments are conducted compared with the conventional methods.

Parameter estimation for ultrasonic echo signals through improved matching pursuit and flower pollination algorithms

Accurate estimations of the time of arrival (TOA) are essential in ultrasonic nondestructive testing. The parameters of ultrasonic pulse echoes are usually estimated by detecting the peak value of the envelope with a Hilbert transformation (HT). However, ultrasonic pulse echoes are likely to be polluted by noise in the propagation process, distorting the envelope signal and preventing the TOA from being accurately estimated. This paper proposes an algorithm for estimating the parameters of ultrasonic echo signals based on joint sparse representation and the flower pollination algorithm (FPA). An improved matching pursuit algorithm is developed to eliminate the noise and improve the envelope extraction of the echo signals. This reduces the superposition of repeated signals and improves the reconstruction accuracy. The signal envelope is then extracted by an HT. Finally, FPA is used to fit the envelope and estimate the parameters. Numerical simulations and experimental results show that the parameters estimated by the proposed method are highly accurate and have a mean square error that is close to the Cramer–Rao lower bound.

Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary

This paper offers a new perspective on the vibrations of discrete bearing faults by focusing on the micro-motion states of rolling elements in spall fault bearings and proposes an improved matching pursuit algorithm for quantitative diagnosis with a high accuracy of atom selection and calculation efficiency. The generation mechanism of the vibration response signal is explained by analyzing the micro-motion status when rolling elements passing through the spall. A concatenation dictionary composed of an impact dictionary as the higher level and step dictionary as the lower level is constructed based on the acceleration variation analysis of the rolling elements. The information output by the higher-level dictionary is used as the input information for the lower-level dictionary to extract the fault features. Only one iteration on the higher-level dictionary is necessary to extract the correct impact atoms, with all subsequent iterative steps assigned to the lower-level dictionary. The advantage is that the influence of high-energy impact components on the extraction of step atoms can be removed. Thereafter, the optimized algorithm based on the concatenation dictionary is applied to the analysis of simulation and experimental signals. The comparative analysis demonstrates that the effective quantitative diagnosis is obtained, while the diagnostic precision and calculation efficiency are improved.

Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis

Vibration signals of gearboxes working under time-varying conditions are non-stationary, which causes difficulties to the fault diagnosis. Based on the techniques of signal sparse decomposition and order tracking, a novel method is proposed to extract fault features from non-stationary vibration signals of gearboxes. The method contains two key procedures, the quasi-steady component separation in angle domain and the impact resonance component extraction in time domain. The sparse dictionary including quasi-steady sub-dictionary and impact sub-dictionary is specifically designed according to the time-frequency characteristics of steady-type fault and impact-type fault. The former sub-dictionary consists of cosine functions and is based on the order spectrum information of angle domain signal. The latter sub-dictionary consists of the unit impulse response of multiple-degree-of-freedom vibration system whose modal parameters are self-adaptively recognized by the method of correlation filtering. An improved matching pursuit algorithm on segmental signal is designed to solve sparse coefficients and reconstruct steady-type fault components and impact-type fault components. The simulation analyses show that the proposed method is capable to process the signal with 30% speed fluctuation and −1.5 dB signal-to-noise ratio (SNR), in which the SNR of impact-type fault components is as low as −14.6 dB. The effectiveness is further verified by experimental tests on a fixed-shaft gearbox and a planetary gearbox.

ECG Signal Denoising Based on Improved MP Algorithm

The electrocardiogram (ECG) signal processing plays an important role in diagnosing cardiopathy. Sparse decomposition provides a new tool for ECG signal processing. Matching pursuit (MP) algorithm uses overcomplete dictionary to decompose the signal, so it can reflect the properties of the signal. In decomposition process, searching a best atom of the dictionary is an optimal problem; Genetic Algorithm (GA) is used to solve this problem. This paper proposed an improved MP algorithm based on GA to denoise the ECG signal, whose dictionary is composed of Gabor atoms. The experiment simulation results show that the proposed method can get a good denosing effect in a shorter time than not use GA algorithm.