# Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission
## 3 Results  
The approaches proposed in this work were tested during structural health monitoring of an ESH 20.90 walking dragline excavator. The reason for the AE testing was a previous damage to the left dragline back legs (a crack in the weld). For data acquisition, the INTERUNIS-IT A-Line 32D DDM system was used, equipped with 30 measuring channels, each of which was connected to resonant sensors (AE GT200) with a bandwidth of 140-200 kHz and a resonance frequency of 180 kHz. The measurement was carried out in the frequency range from 100 to 400 kHz, which ensures a low intensity of the noise environment. AE sensors were installed on the main metal structural dragline components: three sensors for the left and three for the right back legs, eight for the upper boom, four for the lower boom, four for the lower back legs and three for the mast. The distance between the AE sensors varied between 5 and 11 m. Figure 11 shows the location of AE sensors on a dragline boom. Figure 11: Location of AE sensors on a dragline boom. Figure 10: Parametrical model of AE impulse: (**a**) \(RA\) depending on the coordinate of AE source, (**b**) \(RT\) depending on the coordinate of AE source. Intense acoustic noise was measured during dragline operation cycle. The amplitudes of the noise impulses covered almost the entire dynamic range of the AE system from the threshold level of 50 dB to 90 dB. The noise activity reached about 55 AE hits per second on average. Figure 12 shows the values of AE impulse amplitude and cumulative AE hits obtained within an hour. More than 200,000 impulses were registered during an hour of observation. Since the defect emissivity estimated in Section 2.1 was approximately 100 hits per loading cycle, it was obvious that most AE data were caused by noise and this case can hide the presence of a dangerous defect. The most radical noise filtering result was reached with the help of the location procedure. The main source of noise was the ropes movement, and it did not correspond to a specific spatial coordinate displayed on the \"location\". Figure 13 presents the linear location diagram of AE sources on the right back leg. The location diagram was formed by two antennas, the first one included sensor 1 and sensors 2, the second one--sensor 2 and sensor 3. AE sensors were located at 1 m, 9 m, and 15 m from the upper edge of the bac leg, respectively. Figure 13 reveals the result of location of AE data measured during monitoring of the dragline in the period of a day. Figure 13 shows a location result without preliminary data filtering; out of 4,000,000 AE hits measured on average by each measuring channel, about 1.5%, of the order of 16,000 pulses, were located. Even though most of the noisy data was discarded, the location diagram displayed much noise due to the uniform filling which was not typical for real AE sources. Figure 13 depicts the location using the standard criterion for the location amplitude discrepancy. The AE signal pulse amplitude was recalculated taking into account the attenuation coefficient \(\alpha\) from the registration point to the location point where the potential defect was located. Moreover, this amplitude did not exceed the permissible range of the AE sensors sensitivity (6 dB). Location amplitude filtration made it possible to exclude about 60% of false locations from the location diagram. The number of localized AE events decreased to 5125, while a localized cluster was formed in the center of the back leg in the welded joint (\(x\) = 8500-8800 mm). The increase in the number of indications along the location diagram edges had a noisy nature and it could be explained by friction between the fixing elements of the back leg. Figure 12: AE data measured during structural health monitoring of walking dragline excavator: (**a**) amplitudes of AE impulses in a period of an hour, (**b**) AE hits count rate in a period of an hour. Parametric filtering turned out to be the most considerable approach to noise disposal. The result of its application is represented in Figure 13c.