This paper provides a literature review on Adaptive Line Enhancer (ALE) methods based on adaptive noise cancellation systems. Such methods have been used in various applications, including communication systems, biomedical engineering, and industrial applications. Developments in ALE in noise cancellation are reviewed, including the principles, adaptive algorithms, and recent modifications on the filter design proposed to increase the convergence rate and reduce the computational complexity for future implementation. The advantages and drawbacks of various adaptive algorithms, such as the Least Mean Square, Recursive Least Square, Affine Projection Algorithm, and their variants, are discussed in this review. Design modifications of filter structures used in ALE are also evaluated. Such filters include Finite Impulse Response, Infinite Impulse Response, lattice, and nonlinear adaptive filters. These structural modifications aim to achieve better adaptive filter performance in ALE systems. Finally, a perspective of future research on ALE systems is presented for further consideration.

A Review of Adaptive Line Enhancers for Noise Cancellation

As key mechanical components, bearings and gearboxes are employed in most machines. To maintain efficient and safe operations in modern industries, their condition monitoring has received massive attention in recent years. This thesis focuses on the improvement of signal processing approaches to enhance the performance of vibration based monitoring techniques taking into account various data mechanisms and their associated periodic, impulsive, modulating, nonlinear coupling characteristics along with noise contamination. Through in-depth modelling, extensive simulations and experimental verifications upon different and combined faults that often occur in the bearings and gears of representative industrial gearbox systems, the thesis has made following main conclusions in acquiring accurate diagnostic information based on improved signal processing techniques:


1) Among a wide range of advanced approaches investigated, such as adaptive line enhancer (ALE), wavelet transforms, time synchronous averaging (TSA), Kurtogram analysis, and bispectrum representations, the modulation signal bispectrum based sideband estimator (MSB-SE) is regarded as the most powerful tool to enhance the periodic fault signatures as it has the unique property of simultaneous demodulation and noise reduction along with ease of implementation.


2) The proposed MSB-SE based robust detector can achieve optimal band selection and envelope spectrum analysis simultaneously and show more reliable results for bearing fault detection and diagnosis, compared with the popular Kurtogram analysis which highlights too much on localised impulses.


3) The proposed residual sideband analysis yields accurate and consistent diagnostic results of planetary gearboxes across wide operating conditions. This is because that the residual sidebands are much less influenced by inherent gear errors and can be enhanced by MSB analysis.


4) Combined faults in bearings and gears can be detected and separated by MSB analysis. To make the results more reliable, multiple slices of MSB-SE can be averaged to minimise redundant interferences and improve the diagnostic performance.

Enhanced information extraction from noisy vibration data for machinery fault detection and diagnosis

Frequency properties of an adaptive line enhancer

Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are powerful adaptive systems with numerous applications in the fields of signal processing, communications, radar, sonar, seismology, navigation systems and biomedical engineering. An adaptive signal processing algorithm, e.g., the least mean squares (LMS) algorithm and the recursive least square (RLS) algorithm, is used to deal with adaptation of adaptive filters. The adaptive algorithms are expected to be computationally simple, numerically robust, fast convergent and low fluctuant. Unfortunately, none of the adaptive algorithms developed so far perfectly fulfils these requirements. The stability and convergence performance of the widely-used adaptive algorithms also haven’t been fully explored. This work aims to deal with performance analysis and enhancements for the adaptive algorithms and their applications. We first develop a new variable step-size adjustment scheme for the LMS algorithm using a quotient form of filtered quadratic output errors. Compared to the existing approaches, the proposed scheme reduces the convergence sensitivity to the power of the measurement noise and improves the steady-state performance and tracking capability for comparable transient behavior, with negligible increase in the computational costs. We then develop variable step-size approaches for the normalized least mean squares (NLMS) algorithm. We derive the optimal step-size which minimizes the mean square deviation at each iteration, and propose four approximated step-sizes according to the correlation properties of the additive noise and the variations of the input excitation. We next analyze the stability and performance of the transform-domain LMS algorithms which preprocess the inputs with a fixed data-independent orthogonal transform such as

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Performance analysis and enhancements of adaptive algorithms and their applications.

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.

J. Gu

Papers

Performance analysis of the adaptive line enhancer with multiple sinusoids in noisy environment