# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 6 Conclusions  
In this paper, a novel statistical algorithm was proposed that uses the full capability of the tool-set array of conventional guided waves inspection devices to identify defect signals that have been polluted by coherent noise. Laboratory trials confirmed the effectiveness of this algorithm in detecting defects with low/negative SNRs and its dependence on the frequency. Three different methods and two different versions were created for generating the results, in which the thresholding scheme of each was different. In method one, the threshold is a static  
Figure 21: Bar graphs of the Safe zone margins of all methods in the second set of tests. Figure 22: Bar graphs of detection length of the defect using the average value of all of the methods in the second set of tests. Figure 20: The increments achieved using the second version of thresholding in the second set of tests. value set by the inspector. In method two, the assigned threshold to each iteration is a percentage taken from the summation of the amplitude of all of the transducers signals with the same as the time-domain signal generated by the normal propagation routine. In method three, the percentage value is determined by determining the number of transducers with the same phase over the total number of transducers, subtracted by an offset. Two versions of method two and method three are also developed, wherein the second version, the summation of signals with the same sign as the temporal signal was subtracted from the comparisonValue, before determining the final assigned threshold of each iteration. The following conclusions can be drawn with regards to the tests done:  
1. **Method Comparison:** Method one tended to produce the worst results, as it solely depended on a static value set by the user without considering the characteristics of the signals received in each iteration. Methods two and three can achieve a similar result in most cases; however, in method three, the threshold value can be set automatically, which provides a great benefit in one-off inspection scenarios. 2. **Version Comparison:** Version 2 always produced a wider _safe zone_ margin while achieving approximately the same defect detection length using the average values. 3. **Excitation Frequency:** In the first set of tests, by lowering the frequency, a greater _safe zone_ margin and a lower threshold value is required for method one. However, in the second set of tests, the exact opposite occurs. Therefore, method one is highly affected by the regional flexural waves, and high torsional energies are required for detection. However, both of the other methods have an optimum excitation frequency of 34 kHz for the first set of tests, and 37 kHz for the second set of tests. This suggests that all of the methods are more affected by the existing spatial noises caused by the flexural waves in the same iteration rather than the temporal noise of the neighborhood. The second set of tests also clearly illustrates the advantages of method two and method three in detecting defects in locations where a higher level of coherent noise exists. 4. **Safe Margin Zones:** Method three (V2) always achieved the highest _safe zone_ margin, and is therefore the safest method to use. 5. With regard to these results, method three (V2) is the best method since the threshold can be determined automatically, and it achieves the highest _safe zone_ margin. The tests prove that this method can detect defects using both 34 kHz and 37 kHz, depending on the existing coherent noise in the defect location. Furthermore, the algorithm is sensitive to the existing coherent noises in the baseline signal rather than the temporal noise in the tests. This newly developed algorithm can be used to help the inspectors as an additional tool, allowing them an easier interpretation of the results. This is the main reason why all of the developed methods are mentioned.