Adaptive Line Enhancer Research Articles

Diagnosis of Engine Misfiring Based on the Adaptive Line Enhancer

In an automotive engine impulsive sounds and vibration are induced by faults or design constraints which degrade the sound quality of the engine. Thus it is important for an NVH engineer to detect and analyse impulsive sound and vibration signals for both fault diagnosis and also for sound quality assessment. However it is often difficult to detect and identify impulsive signals because of interfering signals such as those due to engine firing, harmonics of crankshaft speed and broadband noise components. These interferences hinder the early detection of faults and improvement of sound quality. In order to overcome this difficulty we present a two-stage ALE (Adaptive Line Enhancer) which is capable of enhancing impulsive signals embedded in background noise.

A Functional Link Network With Higher Order Statistics for Signal Enhancement

A functional link network with higher order statistics is introduced for signal enhancement. The proposed scheme uses the mean-square error (MSE) between higher order statistics of desired signals and filtered output as the learning criterion for training weights in the functional link network. This is motivated by the fact that higher order statistics have a natural tolerance to Gaussian and symmetrically distributed non-Gaussian noises. Results show that the performance of functional link network with higher order statistics is less sensitive to the selection of learning rates than the conventional functional link network and adaptive line enhancement. It is also demonstrated that it can enhance signal more effectively under different noise levels for stationary and nonstationary Gaussian noises

A reduced-rank spectral approach to multi-sensor enhancement of large numbers of sinusoids in unknown noise fields

The enhancement of sinusoids in adverse noisy environments finds applications in many fields. Among the numerous methods that have been proposed to perform this task, few can actually operate without any information on neither the sinusoids nor the noise characteristics. In this paper, we propose a non-parametric spectral approach inspired by the adaptive line enhancer (ALE) principle, which has the capability of detecting and enhancing an arbitrary large number of tones in additive noise of unknown characteristics. Into addition and contrary to the classical ALE, it can take advantage of the extra information provided by multiple sensors so as to significantly increase its performance. The idea essentially consists in a reduced-rank regression between the measured signals and their delayed versions at each frequency of interest, and its implementation is carried out within a general “spectral estimation” framework which encompasses classical estimators such as the WOSA, the multi-taper, and the lag-window. This allows the design of enhancement filters with improved frequency selectivity. The rationale of the method is supported by a statistical performance analysis, and its efficiency is eventually demonstrated on actual industrial signals.

Background noise cancellation of manatee vocalizations using an adaptive line enhancer

The West Indian manatee (Trichechus manatus latirostris) has become an endangered species partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees is desired. Previous research has shown that background noise limits the manatee vocalization detection range (which is critical for practical implementation). By improving the signal-to-noise ratio of the measured manatee vocalization signal, it is possible to extend the detection range. The finite impulse response (FIR) structure of the adaptive line enhancer (ALE) can detect and track narrow-band signals buried in broadband noise. In this paper, a constrained infinite impulse response (IIR) ALE, called a feedback ALE (FALE), is implemented to reduce the background noise. In addition, a bandpass filter is used as a baseline for comparison. A library consisting of 100 manatee calls spanning ten different signal categories is used to evaluate the performance of the bandpass filter, FIR-ALE, and FALE. The results show that the FALE is capable of reducing background noise by about 6.0 and 21.4 dB better than that of the FIR-ALE and bandpass filter, respectively, when the signal-to-noise ratio (SNR) of the original manatee call is -5 dB.

Adaptive filters for detection of gravitational waves from coalescing binaries

In this work we propose use of infinite impulse response adaptive line enhancer (IIR ALE) filters for detection of gravitational waves from coalescing binaries. We extend our previous work and define an adaptive matched filter structure. Filter performance is analyzed in terms of the tracking capability and determination of filter parameters. Furthermore, following the Neyman-Pearson strategy, receiver operating characteristics are derived, with closedform expressions for detection threshold, false alarm, and detection probability. Extensive tests demonstrate the effectiveness of adaptive filters both in terms of small computational cost and robustness.

Background noise reduction of manatee vocalizations

The West Indian manatee (Trichechus manatus latirostris) has become an endangered species in the United States partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees is desired. Previous research has shown that background noise limits the detection range (which is critical for practical implementation). By improving the signal-to-noise ratio of the vocalization signal, it is possible to extend the detection range. The finite impulse response (FIR) structure of the adaptive line enhancer (ALE) can detect and track narrow-band signals buried in broadband noise. In this paper, a constrained infinite impulse response (IIR) ALE, called a feedback ALE (FALE), is implemented to reduce the background noise. In addition, a bandpass filter is used as a baseline for comparison. A library consisting of 100 manatee calls spanning ten different signal categories is used to evaluate the performance of the bandpass filter, FIR-ALE, and FALE. The results show that the FALE is capable of reducing background noise by about 6.0 and 21.4 dB better than that of the FIR-ALE and bandpass filter, respectively, when the signal-to-noise ratio (SNR) of the original manatee call is −5 dB.

Speech Noise Reduction System Based on Frequency Domain ALE Using Windowed Modified DFT Pair

The speech noise reduction system based on the frequency domain adaptive line enhancer using a windowed modified DFT (MDFT) pair is presented. The adaptive line enhancer (ALE) is effective for extracting sinusoidal signals blurred by a broadband noise. In addition, it utilizes only one microphone. Therefore, it is suitable for the realization of speech noise reduction in portable electronic devices. In the ALE, an input signal is generated by delaying a desired signal using the decorrelation parameter, which makes the noise in the input signal decorrelated with that in the desired one. In the present paper, we propose to set decorrelation parameters in the frequency domain and adjust them to optimal values according to the relationship between speech and noise. Such frequency domain decorrelation parameters enable the reduction of the computational complexity of the proposed system. Also, we introduce the window function into MDFT for suppressing spectral leakage. The performance of the proposed noise reduction system is examined through computer simulations.

Damage Identification of an Automotive Engine Based on Detection of Impact Vibration Using an Adaptive Filter

In an automotive engine, faults induce impulsive vibrations and thereby degrade engine performance, making it important for an automotive engineer to detect and analyze impulsive vibration signals for fault diagnosis. However, detecting and identifying impulsive signals is often difficult because of interfering signals such as those due to engine firing, harmonics of crankshaft speed and broadband noise components. These interferences hinder early fault detection. To overcome this difficulty we present a two-stage ALEF (Adaptive Line Enhancer Filter) that is capable of enhancing impulsive signals embedded in background noise. This method is used to pre-process signals prior to time-frequency analysis via higher order methods such as the combined higher order time-frequency.

A Noise Reduction System for Wideband and Sinusoidal Noise Based on Adaptive Line Enhancer and Inverse Filter

A noise reduction technique to reduce wideband and sinusoidal noise in a noisy speech is proposed. In an actual environment, background noise includes not only wideband noise but also sinusoidal noise, such as ventilation fan and engine noise. In this paper, we propose a new noise reduction system which uses two types of adaptive line enhancers (ALE) and a noise estimation filter (NEF). First, the two ALEs are used to estimate speech components. The first ALE is used to reduce sinusoidal noise superposed on speech and wideband noise, while the second ALE is used to reduce wideband noise superposed on speech. However, since the quality of the speech enhanced by two ALEs is not good enough due to the difficulty in estimating unvoiced sound using the two ALEs, the NEF is used to improve on noise reduction capability. The NEF accurately estimates the background noise from the signal occupied by noise components, which is obtained by subtracting the speech enhanced by two ALEs from noisy speech. The enhanced speech is obtained by subtracting the estimated noise from noisy speech. Furthermore, the noise reduction system with feedback path is proposed to improve further the quality of enhanced speech.

A Proposal of an Adaptive Line Enhancer by Using Wavelet Transform

Adaptive line enhancer which is one of the application of adaptive digital filter can extract only periodic signals buried in a broad-band noise, under the condition of the frequencies of the periodic signals are unknown. However it is known that adaptive line enhancer gets impossible to remove noise for a certain period after a strong pulse noise is put to the adaptive line enhancer with input signal. In this paper, an adaptive line enhancer by using wavelet transform is proposed to get over the problem. Here, discrete wavelet transform is used for the proposed method, however, the discrete wavelet transform based on multiresolution analysis has a demerit of the lack of shift invariance inevitably. Hence considering the demerit of discrete wavelet transform, in this paper, redundant discrete wavelet transform is used which realizes the shift invariance. The effectiveness of the proposed method is shown through simulations. Moreover voice signals that are sampled from human voice are used in simulation to remove noise to show the effectiveness of the proposed method even for voice signals.

ACQUISITION OF SARSAT INFORMATION USING AN AUTO-CORRELATION BASED ADAPTIVE LINE ENHANCER

This paper develops a new approach to the detection of the emergency locator transmitter (ELT) signal using an adaptive line enhancer (ALE). The input signal to the ALE is replaced by its autocorrelation function (ACF) because noise affects the central samples of the autocorrelation function only while it affects all samples of the noisy signal. This gives the ALE the ability to get rid of noise, easily. The output ACF is then used in the spectral estimation and detection of the ELT signal. This approach is related to the signal processing using Higher Order Statistics (HOS) since the ACF is the used input. The paper also compares the results of the new approach to other different previously used methods foe ELT signal detection. The first method uses the signal xk as an input to the ALE, and the second method uses [Formula: see text]. Results illustrate the superiority of the proposed method over the other two methods. A comparison study between the performance of two types of ALE; Fixed pole radius and variable pole radius ALEs in detecting the ELT signal, is introduced.

Differential fuzzy filtering for adaptive line enhancement in spread spectrum communications

This paper presents an estimator/subtractor narrow band interference (NBI) canceller based on fuzzy logic and applied to DS-CDMA systems. The designed fuzzy canceller introduces the slow varying nature of the NBI in its rule base. In this way, we obtain an interference suppressor with the following features: (i) fusion of different NBI models in its rule base; (ii) fast adaptation capability; (iii) low computational burden, and parallel structure; (iv) linguistic mapping between its input and output variables; and (v) non-parametric conditional mean interference estimation. Two criteria have been derived to obtain the “working region” of the fuzzy canceller, which has been adapted using a reduced cost recursive least squares (RLS) algorithm. Simulation results show that the fuzzy filter is able to cope with whatever shape of interference in its working region successfully.

Background noise cancellation for improved acoustic detection of manatee vocalizations

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees, so that a collision can be avoided, is desired. A practical implementation of the technology is dependent on the hydrophone spacing and range of detection. These parameters are primarily dependent on the manatee vocalization strength, the decay of the signal's strength with distance, and the background noise levels. An efficient method to extend the detection range by using background noise cancellation is proposed in this paper. An adaptive line enhancer (ALE) that can detect and track narrow band signals buried in broadband noise is implemented to cancel the background noise. The results indicate that the ALE algorithm can efficiently extract the manatee calls from the background noise. The improved signal-to-noise ratio of the signal can be used to extend the range of detection of manatee vocalizations and reduce the false alarm and missing detection rate in their natural habitat.

Background noise cancellation for improved acoustic detection of manatee vocalizations

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees, so that a collision can be avoided, is desired. Practical implementation of the technology is dependent on the hydrophone spacing and range of detection. These parameters are primarily dependent on the manatee vocalization strength, the decay of the signal strength with distance, and the background noise levels. An efficient method to extend the detection range by using background noise cancellation is proposed in this paper. An adaptive line enhancer (ALE) that can detect and track narrowband signals buried in broadband noise is implemented to cancel the background noise. The results indicate that the ALE algorithm can efficiently extract the manatee calls from the background noise. The improved signal-to-noise ratio of the signal can be used to extend the range of detection of manatee vocalizations and reduce the false alarm and missing detection rate in their natural habitat.

Adaptive modelling of transient vibration signals

Conventional vibration signal processing techniques are most suitable for stationary processes. However, most mechanical faults in machinery reveal themselves through transient events in vibration signals. Time-series modelling, including autoregressive moving average (ARMA) modelling and autoregressive (AR) modelling, is an efficient approach for transient signal analysis. Based on the adaptive prediction technique, this paper applies the principle of the adaptive line enhancer (ALE) to the modelling of transient vibration signals. The time-series models, adaptive algorithms and the rational time–frequency transfer function are investigated in the paper. Simulation and experimental studies with different time–frequency–amplitude distributions and transient vibration responses are described. The results show that the adaptive modelling method can trace the time–frequency signal and extract dynamic features such as time–frequency distributions and time–amplitude distributions from sample signals. Given the simple programming and potentially easy implementation in on-line applications, this method should have application in machine monitoring and fault diagnosis.

A Noise Reduction Method for Non-stationary Noise Based on Noise Reconstruction System with ALE

A noise reduction technique to reduce background noise in noisy speech is proposed. We have proposed the noise reduction method which uses a noise reconstruction system. However, since a residual speech signal is included in the input signal of a noise reconstruction filter (NRF) used for reconstructing the background noise, the long time average value of error signal for estimating the background noise is needed not to estimate the speech signal. Therefore, the ability of tracking the non-stationary noise is decreased. In order to solve this problem, we propose the noise reconstruction system with adaptive line enhancer (ALE). Since ALE works to obtain the signal occupied by noise components, the input signal of the NRF includes only a few speech components. Therefore, we can give the high tracking ability to NRF.

101 転がり軸受音響診断のためのノイズキャンセラとアダプティブラインエンハンサーの特性(セッション1 センサ技術・信号処理)

101 転がり軸受音響診断のためのノイズキャンセラとアダプティブラインエンハンサーの特性(セッション1 センサ技術・信号処理)

Design of an adaptive line enhancement system using a variable step-size affine-projection algorithm

This paper presents design of an adaptive line enhancement (ALE) system for improving sensor response using a variable step-size affine-projection algorithm (VSS APA). ALE is an adaptive technique that may be used to detect a periodic signal buried in a broadband noise background such as in rotating machinery fault diagnosis. However, most of the conventional methods for ALE system are based primarily on an adaptive filter with the least-mean-square (LMS) error algorithm. Unfortunately, convergence speed is limited when a filtering plant is varied, because the learning process of the adaptive algorithm fails to respond quickly enough to the changing operational conditions. This study proposed a VSS APA for improving both the convergence speed and the performance of the ALE system. Two applications were conducted to compare the performance of the proposed algorithm and various traditional adaptive filtering algorithms. The first application used the proposed ALE system to improve the response of a wheel speed sensor output signal; the other was used for reducing the background noise during rotating machinery fault diagnosis. Both the experimental results indicated that the ALE with VSS APA has an effective performance and convergence for both applications.

Parallel Genetic Algorithm based Adaptive Line Enhancement Schemes for Extraction of Power Frequency Signals

In this article, we propose two new adaptive line enhancement schemes for tracking the power frequency signal corrupted with broad-band noise. The first scheme named as Hybrid Parallel Genetic Algorithm Based Line Enhancer (HPGABLE) employs Parallel Genetic Algorithm (PGA) and Least Mean Square (LMS) Algorithm to obtain the optimal set of filter coefficients while the delay is selected on an ad hoc basis. In the second proposed Recursive Hybrid Parallel Genetic Algorithm Based Line Enhancer (RHPGABLE) scheme the delay as well as the filter coefficients are estimated recursively. In the recursion of the proposed RHPGABLE algorithm, Parallel Genetic Algorithm (PGA) based on coarse grained approach is employed for estimating the delay while the filter coefficients are estimated by PGA and LMS Algorithm. RHPGABLE is an unsupervised scheme in the sense that no a priori knowledge of delay, filter coefficients and the associated training signal component is assumed to be available. The proposed schemes could be successfully tested on both synthetic data as well as the data obtained from the Steel Melting Shop of Rourkela Steel Plant.

A novel robust current control approach using adaptive line enhancer for three‐phase current‐source active power filter

AbstractAn effective system control method is presented for applying a three‐phase current‐source PWM converter with a deadbeat controller to active power filters (APFs). In the shunt‐type configuration, the APF is controlled such that the current drawn by the APF from the utility is equal to the current harmonics and reactive current required for the load. To attain the time‐optimal response of the APF supply current, a two‐dimensional deadbeat control scheme is applied to APF current control. Furthermore, in order to cancel both the delay in the two‐dimensional deadbeat control scheme and the delay in DSP control strategy, an Adaptive Line Enhancer (ALE) is introduced in order to predict the desired value three sampling periods ahead. ALE has another function of bringing robustness to the deadbeat control system. Due to the ALE, settling time is made short in a transient state. On the other hand, total harmonic distortion (THD) of source currents can be minimized compared to the case where ideal identification of the controlled system can be made. The experimental results obtained from the DSP‐based APF are also reported. The compensating ability of this APF is very high in accuracy and responsiveness although the modulation frequency is rather low. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 50–61, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20014