Performance Of Wiener Filter Research Articles

Investigation of bearing faults in three phase induction motor using wavelet de-noising with improved Wiener filtering

Bearing fault diagnosis in an induction motor, especially at nascent stage has become inevitable and captious to avoid unexpected shut down of the industrial process. Many researchers have concentrated on various monitoring techniques including vibration, temperature, chemical and current monitoring. In this paper, an improved bearing fault detection using motor current signature analysis (MCSA) has been presented. In the proposed work, the bearing fault signature is extracted from stator current using improved Wiener filter cancellation. Performance of Wiener filter is improved using two stage process. The side band effects of filter is removed using Kaiser window and the higher order noise due to filtering process is removed with wavelet de-noising technique. Different categories of bearing failures are examined with and without de-nosing using pre-fault component cancellation (noise cancellation). Moreover, fault indexing based on standard deviation (SD) and energy (E) value of noise canceled stator current is proposed. The proposed bearing fault detection topology is examined using simulations and experiments on a 2HP induction motor under different load condition.

Hybrid Lower-Complexity Wiener Filter for Pilot-Based Channel Estimation for C-RS in LTE-A DL System

This paper presents a pilot-based lower complexity channel estimation for Long Term Evolution Advanced (LTE-A) Cell-specific Reference Signals (C-RS). The proposed system is a hybrid one, based on using 2 × 1-D Wiener filter for noise filtering at pilot locations only with cubic spline interpolation for data symbol locations, with accuracy that almost matches the 2-D Wiener filter and Wiener interpolation under certain channel and noise conditions, and at reduced computational complexity. The performance of the proposed scheme is evaluated in terms of Mean Squared Error (MSE) under various channel conditions. Complexity analysis show that the proposed scheme requires 8.8% of the number of computations needed by 2-D Wiener filtering with Wiener interpolation, while still matching its performance up till specific noise and channel conditions. In addition, 2 × 1-D Wiener filter performance is proven to be sub-optimal when compared with the performance of 2-D Wiener filter. The obtained results show that the best noise filtering method is a combination of both moving average and Wiener filtering, where the lower bound is a function of both Symbol-to-Noise-Ratio (SNR) and the channel statistics. Simulations in real-life LTE-A scenarios confirms the feasibility of the proposed channel estimation scheme. Analytical results wherever possible and, in general, simulation results are presented.

Comparative Performance of Wiener Filter and Adaptive Least Mean Square-Based Control for Power Quality Improvement

This paper presents a three-phase shunt active power filter (SAPF) for mitigating power quality problems at the distribution level. Power quality problems such as harmonics, reactive power, and unbalancing in the loads are mitigated for both unity power factor and voltage regulation modes. A three-phase voltage-source converter (VSC) is used as an SAPF for performing all these functions. The linear optimum discrete time filtering technique namely Wiener filtering-based control algorithm is developed for the extraction of reference supply currents from distorted load currents. The performance of Wiener filter is compared with least mean square (LMS) adaptive filter-based control algorithm. Experimental validation of proposed control algorithm has been performed on a prototype developed in the laboratory. The filtering action of SAPF is able to achieve the total harmonic distortion (THD) of supply current within the limit specified by an IEEE-519 standard.

Performance of Wiener Filter and Adaptive Filter for Noise Cancellation in Real-Time Environment

Form the past several decades’ noise cancellation in speech signal gains researchers’ attention. Several techniques were developed for noise cancellation among them optimal wiener filter can be the one of the most fundamental approach for noise cancellation. Later on adaptive filter was introduced to attain better performance. This paper shows the capacity of wiener filter and adaptive filter for removal of noise by estimating the signal by means of removing the noise signal form the corrupted signal.Wiener filter plays a central role in wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalization and system identification. In this paper the performance of wiener filter and adaptive filter for removal of noise in the presence of real time environment are compared. In the existing papers the authors have proposed the theory of wiener filter and adaptive filter algorithms in real time environment like recorded speech. So this is paper is going to take the part of the existing paper and going to perform the noise cancellation. In order to measure the performance step size is the main factor for the convergence speed and mean square error. Wiener filter provides better performance for noise cancellation but it requires large no. of computations i.e., complexity and cost of the system is going to increase, so adaptive filter is the alternate approach for removal of noise with moderate complexity and cost. The simulation result clearly shows that wiener filter gives the better performance but due to high cost adaptive filter is the choice of many applications. This paper is going to discuss about wiener filter theory, wiener filter problem, solution to optimal filtering, adaptive filtering, adaptive algorithm, study of wiener filter and adaptive filter for noise reduction etc.

Performance limits in subband beamforming

This paper analyzes subband beamforming schemes mainly aimed at speech enhancement and acoustic echo suppression applications such as hands-free telephony for both mobile and office environments, Internet telephony and video conferencing. Analytical descriptions of both causal finite-length and noncausal infinite-length subband microphone array structures are given. More specifically, this paper compares finite Wiener filter performance with the noncausal Wiener solution, giving a comprehensive theoretical suppression limit. It is shown that even short filters will yield a good approximation of the infinite solution, provided that the element spacing and temporal sampling is matched to the frequency band of interest. Typically, 10-20 FIR taps are sufficient in each subband.

Reconstruction of turbulence-degraded images using the vector Wiener filter

Adaptive optics and speckle imaging are common methods for improving Fourier domain signal-to-noise ratio (SNR) in astronomical images. These techniques may benefit from linear processing to decon- volve blurring due to the attenuation of high spatial frequencies in the compensated images. Typical linear deconvolution methods require an explicit estimate of the random atmospheric-optical system point spread function or optical transfer function (OTF). In addition, a priori knowledge of the object class and noise are not used in an optimal manner. We apply a vector Wiener filter to photon-limited images degraded by atmo- spheric turbulence to demonstrate the potential advantages of optimal deconvolution processing. This filter incorporates model-based informa- tion about object, OTF, and noise. To our knowledge, this is the first application of complete OTF correlation statistics to the reconstruction of turbulence-degraded images. Computer simulation of binary star images show the vector Wiener filter provides superior reconstructions when compared to the traditional scalar Wiener filter for non-wide-sense sta- tionary objects. Much of this performance improvement can be attributed to variance reduction in the noisy Fourier data at spatial frequencies where the mean OTF is severely attenuated. However, vector Wiener filter performance is substantially degraded with respect to both mean square error and mean square phase error at spatial frequencies where the OTF SNR is less than unity. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)01009-5)