Classification of ultrasonic signals
In ultrasonic non-destructive testing it is very difficult to detect flaws in materials with coarse-grain structure. The ultrasonic signals measured on these materials contain echoes which are very similar to fault echoes. These echoes arise from grains which are contained in the material. For the detection of flaws various methods for suppressing echoes from grains have to be used. In this work we used the method for filtering ultrasonic signals based on discrete wavelet transform. For the classification of ultrasonic signals in A-scan we used a pattern recognition method called support vector machines. In this study we classify signals with fault echoes, echo from weld and back-wall echo. Ultrasonic signals were measured on materials used for constructing aeroplane engines. The experimental results indicate the performance of the proposed approach.
Algorithms for suppressing ultrasonic backscattering from material structure.
In pulse-echo ultrasonic inspection the backscattering from the material structure appears in the received ultrasonic images as clutter, often referred to as grain noise, which impairs the inspection results. A toolbox including algorithms for suppressing ultrasonic clutter is presented in the paper. Several processing algorithms capable of suppressing grain noise have been proposed, of which the split spectrum processing (SSP) probably is the most renowned. The classical SSP technique applies a filter bank to some frequency band that has to be precisely known in advance, to obtain a set of narrow-band signals that are tested for mutual correlation using some statistical operation. A number of SSP algorithms with different statistical operations are included in the toolbox. A completely different approach is to use explicit statistical models of grain noise and defects and to design an optimal filter based on those models. A simple such algorithm, based on noncoherent detection (NCD) known from communications, is also included in the toolbox. The toolbox, implemented in Matlab, is provided with a user-friendly graphical interface facilitating comparison of the algorithms.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.