Further advancements in the field of SSP are therefore required before this technique can become commercially viable. Keywords: Split spectrum processing, SSP, grain noise, ultrasonic signal processing. However when the wavelength of the signal is in the same order of magnitude as constituent particles of the propagating medium, scattering may overwhelm the return echo.
A classic analogy is the light in a headlamp, which is scattered by small droplets in fog. In this case the droplets have the same order of magnitude in size as the wavelength of the light, causing severe scattering. By increasing the transmitter power brighter lights the scattering simply increases proportionately, and no improvement is made to visibility [3]. In the case of industrial ultrasonics, the scattering of a signal is due to the metallurgical grain structure of the material being tested.
This scattering results in grain noise. Increasing the wavelength used can reduce this grain noise to tolerable limits. However increasing the wavelength also sets a lower limit to the detectability of small flaws. A casting may have large grains, with the maximum grain size reaching over microns. To eliminate the grain noise from ultrasonic inspections would therefore require wavelengths one order of magnitude larger than microns, i.
Planar flaws smaller than 4mm therefore become difficult to detect. In addition to this, flaws are not typically planar, but follow grain boundaries. The resulting flaws will therefore have an extremely small planar area, even in the case of large flaws.
A real flaw will reflect all wavelengths smaller than the flaw size. However in the case of a grain boundary, not all smaller wavelengths are reflected. This has been shown graphically in Figure 1, where a real flaw is shown as the top solid line, while a grain boundary is shown as the bottom dotted line.
Multiple frequencies in the form of a chirp signal in the figure can then be transmitted onto these two boundaries. In the case of a real flaw all frequencies are reflected, while in the case of a grain boundary only a subset of the frequencies are reflected. This phenomenon has been termed frequency diversity [4]. Fig 1: Frequency diversity of a real flaw versus a grain boundary. Split Spectrum Processing SSP uses this frequency diversity to determine if a signal originates from a real flaw or a grain boundary.
The implementation of SSP therefore makes use of multiple frequencies to inspect a component. This is best achieved by the use of a broadband signal such as an impulse for excitation of the probe , which can then be decomposed into a number of frequency bands. The general algorithm for SSP can also be optimised by performing the filtering in the frequency domain, resulting in a flow diagram for SSP as shown in Figure 2.
Fig 2: Flow diagram for Split Spectrum Processing. For the purposes of this discussion, a sample RF wideband signal is used throughout this paper, as shown in Figure 3. This signal was generated using a 0. The casting was machined with a 5mm flat bottom hole, 11mm deep from the back wall of the block, as shown in Figure 4.
Fig 3: Sample signal captured with a 0. Fig 4: Ultrasonic probe on an austenitic steel block. For this specific signal, the signal amplitude reflected from the 5mm flat bottom hole and the noise level in the previous 15mm of material see Figure 3 is compared to determine if the signal to noise ratio has been improved with the various algorithms. For the unprocessed signal, the following values were measured:. The 5mm hole signal peak is approximately - 0.
The sample signal can now be processed using SSP techniques to determine the capabilities of SSP, and for a comparison of the various recombination algorithms used in SSP. The first step in SSP is to obtain a number of frequency components from a specific ultrasonic signal. This can be achieved by filtering the original signal through a set of band-pass filters. The reasoning for this is that each discernable frequency in the frequency spectrum of the digitised signal can be used.
However this improvement quickly asymptotes, while the required processing power increases linearly with each additional filter[5]. However M. Pollakowski et al. In some instances, the filters have been proposed that have an upper limit less than that of the transducers, since grain noise is more significant at higher frequencies[9][10], i. An exception to equal bandwidth filters is the use of filters where the bandwidth is proportional to frequency[6] i.
This method is also used for the optimisation of wavelet filtering in SSP. A number of references [5][19][20][21] make use of wavelet transforms or wiener filters using a modified SSP algorithm, where the filter is a Gaussian filter, but the bandwidths are frequency dependant. The reasoning for the use of the Gaussian filter was not explicit in any of the papers, except when mentioning the ease of implementation.
Fig 5: Overlap of filter pass-bands. For purposes of comparing the various SSP algorithms, the signal presented in Figure 3 has been filtered with the use of six Gaussian filters, resulting in six filtered signals as shown in Figure 6. The filters intersected at the -6dB point, but the bandwidths were selected such that each filter contained equal amounts of signal energy. These days a lot of electronic music styles are based around 'bass' sounds that don't necessarily just occupy the low-end of the mix - sometimes they include everything from the sub bass frequencies up to the very highest of highs!
These basses can sound massive but can be tricky to mix. We can make life easier by splitting these kinds of parts into two or more bands, which allows us to process the sub bass separately from the rest of the signal. There are plugins dedicated to this very task: for example, Blue Cat Audio's MB-7 Mixer enables you to split a signal into up to seven bands and process each one with its own plugins.
It's also possible to split signals into separate bands manually by setting up your own crossover filter, and in this example, we'll see how this can be achieved using FabFilter's excellent Pro-Q 2 equaliser. For the highest degree of accuracy we'll use the effect's linear phase mode, which means our filtering won't affect the phase of the original signal. For more on making and mixing sub bass, pick up Future Music , which is on sale now. If you examine this sound with a spectral analyser, you'll see that it contains a broad range of frequency content, right down into the sub frequencies.
This makes it tricky to mix, so let's split the sub part onto its own channel. Double-click the main display to create a new filter band, click the filter type on the left-hand side of the spectral analyser and select High Cut. Step 3: Double-click Freq and set the frequency to Hz.
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