Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNRimp) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect.

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

This paper proposes an adaptive equalization algorithm for asynchronous cooperative communications in ad hoc networks, where amplify-and-forward relays are adopted, each of which is equipped with single antenna. Adaptive equalization technique is carried out at the destination node in this paper to remove inter-symbol interference, which is caused by the retransmissions of the asynchronous relays. Least mean squares (LMS) has been regarded as an effective adaptive method, but it has difficulty in obtaining the optimal solution. In this paper, we present a hybrid adaptive scheme by combining particle swarm optimization (PSO) with conventional LMS algorithm, where PSO is utilized to search the optimal solution during the iterative process, and LMS is employed to avoid the local convergence, which is usually caused in PSO. Numerical simulation results show that, the proposed scheme outperforms conventional LMS algorithm in convergence performance over Rayleigh flat fading channel, and meanwhile, a signal–noise-ratio gain of 6 dB or so is obtained when BER is 10−3.

A PSO-Based Hybrid Adaptive Equalization Algorithm for Asynchronous Cooperative Communications

This paper presents an efficient design of Adaptive filters which uses enhanced NLMS algorithm for eliminating noise added by mean of various communication media or any other noise sources. By using the appropriate weights, Adaptive filter estimates and remove the estimated noise signal from the available information. LMS and Normalized LMS are two most efficient algorithm for noise cancelation. Normalized form of LMS algorithm is considered more effective as it normalizes with the power of the input. The concept of memory preallocation for variables has been introduced to enhance the computational speed of filtering system. The proposed algorithm is successfully implemented using M Code and performance measures like SNR and MSE are calculated for varying step size and filter order. The SNR value ranges from 8.26 dB to 7.22 dB and MSE ranges from 0.087 to 0.1173.

Adaptive Noise Cancellation using Modified Normalized Least Mean Square Algorithm

The paper presents a novel algorithm for the solution of Multimodal Optimization problems. The proposed method is hybrid of Nelder Mead algorithm and gradient method. The algorithm has direct application to problems encountered in the area of signal processing. The proposed method is applied for the estimation of taps in the design of adaptive filters. The convergence of the proposed algorithm is compared with the convergence trajectories of Steepest Descent method and found to be superior. The algorithm is presented for second order optimization problem and was applied for the design of three tap Wiener filter.

A method for multimodal optimization with application to signal processing

The invention discloses a pump noise elimination method using empirical mode decomposition and particle swarm optimization algorithm, This method is based on the assumption that the pump noise is a group of linear combinations. After the pump noise samples are extracted, the extracted pump noise samples are decomposed into a group of signals by using the empirical mode decomposition, and the bestlinear combination coefficients of this group of signals can be found by particle swarm optimization algorithm to update the pump noise samples and improve the denoising effect. In the invention, thecurrent pump noise sample is corrected in a weighted form in a limited number of denoising cycles so as to gradually converge to the pump noise waveform in a changed unit period in a limited number ofiterations, so as to adapt to the slow change of the pump noise in the long-time operation process of the system.

Empirical Mode Decomposition (EMD) and Particle Swarm Optimization (PSO) are used to eliminate the pump noise

Industrial noise is generated due to the number of sources that interferes with the signals. The source and weight of noise signals are hard to analyze hence a collective form of noise called Gaussian Noise is considered in this paper. This noise is collective form of noise signals that arise in industrial and transmission scales of signal processing. We have implemented wiener filter, least-mean-square algorithm, normalized LMS algorithm for denoising the noisy signals. In this paper we propose a particle swarm optimization (PSO) based wiener filter for enhancement of filtering. A comparative analysis is performed on these algorithms and generated the MSE and PSNR values of signals..

http://ieeexplore.ieee.org/iel7/6937465/6949766/06949863.pdf

Performance analysis of industrial noise cancellation with pso based wiener filter using adaptive LMS & NLMS

Speech Signals have high range of variation in amplitudes and frequency. These acoustic signals with diverse properties are hard to recognize and filter if mixed with noise. To separate noise from original signal, the artifact peaks are separated from original signal and discarded. In this paper, the ICA method of signal denoising is used to differentiate the speech signal from periodic noise and Empirical Mode Decomposition method is proposed to generate the components of signal. The IMF(s) of signal is the non-linear descending order of frequency components that have been filtered for better SNR. Filtering with wiener filter has amended output but also results in loss of information. The selection of IMF(s) for signal regeneration when optimized using objective function of PSO, the information of original signal was dramatically preserved with suppressed noise. The system is tested on 4 example signals and proposed technique illustrates lower mean square error and higher SNR compared to wiener and ICA.

http://www.sersc.org/journals/IJSIP/vol7_no6/30.pdf

K R Rekha

Papers

Performance analysis of industrial noise cancellation with pso based wiener filter using adaptive LMS & NLMS

Novel Approach for Industrial Noise Cancellation in Speech Using ICA-EMD with PSO