3.5.7. AE Testing of the Shaft

The AE signal, characteristic for the friction process, is a continuous random process characterized by a distribution of pulse amplitudes close to normal, which is not typical for cracks. It is found that higher RMS amplitudes of the acoustic signal are observed in roller bearings with a long service life, which allows us to conclude that the RMS amplitude of the AE signal characterizes the integral degree of wear of the shaft surface [24]. Figure 22 shows the signals obtained in the diagnosis of roller shafts that were in service for 2 and 9 years. The corresponding RMS amplitudes are 0.9 mV and 2.2 mV.

**Figure 22.** AE signals obtained during the diagnostics of shafts of roller bearings being in operation for (**a**) 2 and (**b**) 9 years. The corresponding RMS amplitudes are 0.9 mV and 2.2 mV.

The appearance of individual pulses of large amplitude (up to 81 dBAE) against the background of a continuous signal may indicate the beginning of the destruction of the surface of the shaft in the support block during frictional contact (Figure 23).

**Figure 23.** AE signals received during the diagnosis of shafts. The beginning of the destruction of the shaft surface during frictional contact.

Acoustic emission that is caused by the formation and growth of cracks has a fundamentally discrete and aperiodic nature. When the crack grows, as a rule, high-amplitude signals are generated, with a certain characteristic distribution of the parameters of the AE data stream. AE testing of the support blocks on 2 shafts being in operation for 11 and 13 years revealed AE sources with amplitudes up to 78 dBAE and activity of about 10–20 s−<sup>1</sup> (Figure 24), which, judging from the waveforms and the distribution of the AE parameters, correspond to active fatigue cracks. Analysis of the linear location showed that the sources of AE are located in the region of the fillets, that is in places with the greatest concentration of stresses. The identified sources of AE turned out to belong to the I and II hazard classes and for this reason did not undergo the confirmation procedure.

At present, the design of SHM-system for the roller bearings testing is under consideration.

**Figure 24.** Waveforms of AE signals recorded on shafts of 2 roller bearings (**a**,**b**) and linear location results (**c**,**d**).

#### *3.6. Dragline Excavator*

On the territory of Russia, handling equipment is often used in difficult climatic conditions. Fluctuations of the temperature, cyclic operating loads lead to the nucleation and propagation of the

defects in a load-carrying structure component, which reduces its residual life and, as a result, can lead to an emergency destruction of the structure.

Using AE monitoring can prevent a sudden occurrence of an accident, reduce economic damage and prevent human casualties. Despite the fact that the operation of the lifting equipment is accompanied by a high level of acoustic noise, AE monitoring is possible by optimizing the location of the sensors and applying special methods of data processing [25,26].

Studies were conducted to assess the feasibility of developing an AE monitoring of metal structural elements of a walking dragline excavator. The work of such dragline excavator is carried out all year round, continuously in 3 shifts. Most of the dragline excavators on the mine where the studies were conducted worked out their normal service life; however, it continues to be actively used.

The most stressed and, as a consequence, the most susceptible to the formation of defects are the struts. Emergency situations with dragline excavator lead to downtime, whose duration, for example, in 2016 at one mine was 812 h. Currently, in accordance with the recommendations of manufacturers two times a year periodic diagnostic tests of dragline excavator by ultrasonic method are performed.

Research work was carried out on booms and struts of dragline with bucket capacity—20 m3 and boom length—90 m, which previously had damage at the left strut near the weld (Figure 25). The work was carried out without dragline excavator decommissioning. A 30-channel A-Line DDM AE system and GT200 AE sensors with a working frequency range of 130–200 kHz were used. A-Line DDM system provides the principle of digital data transmission. The system consists of several sequentially connected measuring lines. The amplification of AE signals, filtering, AD conversion and threshold impulse detection and calculation of AE parameters are carried out in the AE module, which is located next to the AE sensor directly in the tested structure. AE sensors were installed on the main structural elements of the dragline: 3 on the left and right struts, 8 on the upper boom belt and 4 on the lower boom belt, 4 on the lower struts and 3 on the column. The distances between the AE sensors were from 5 to 15 m. In addition, 5 AE sensors were mounted on the anchor links where the zone location was carried out, since they consist of structural elements between which there is no acoustic contact. AE monitoring of a rope was not carried out, as the damage to the rope does not lead to prolonged downtime. In addition, the rope is a movable part, that greatly complicates its AE testing. Data was collected directly during the work of the dragline.

At the first stage, the frequency range and threshold were selected and the acoustic properties of the monitored object were determined. Two frequency ranges were tested: 30–500 and 150–500 kHz. When using a wide frequency range (30–500 kHz), an extremely high AE activity was observed, which did not have a visible dependence on the operating mode of the dragline excavator, that is, on the value of the load. Such noise is associated with the continuous operation of the equipment (working winch, loose structural elements, engine operation, vibration, etc.).

Therefore, a filter of 150–500 kHz was used. Complete noise detuning by the threshold method was still impossible, since the noise reached 100 dBAE. As a compromise, a threshold value of 50 dBAE was chosen. With such settings, the nature of AE activity became synchronized with the cycle of operation of the dragline excavator (Figure 26). The greatest activity was observed with excavation of the ground and with unloading the bucket, that is at the loading of the structure, when cracks could grow and at structure unloading, when the crack edges can rub each other.

**Figure 25.** Tested dragline excavator.

(**a**)

**Figure 26.** *Cont.*

**Figure 26.** AE activity when using 30–500 kHz (**a**) and 150–500 kHz (**b**) frequency filters. Correlation of AE hit count with the cycle of operation of the dragline excavator when using 150–500 kHz frequency range (**c**).

Measurement of the acoustic properties of the monitored excavator was carried out using the Hsu-Nielsen source. The following values were obtained: velocity was 3200–3700 m/s and attenuation was 1.5–2.8 dB/s. The recommended maximum distances between AE sensors that ensure a linear location are from 4.5 to 9 m.

At the second stage, within 2 days, the AE data was collected directly for subsequent detailed analysis. The distribution of the AE signal amplitudes was analyzed. For all structural elements except for the struts, the differential distribution of the amplitudes was exponential, that is corresponding to a random noise process. On the left and right struts in the range of amplitude values of 80–85 dBAE, the distribution was significantly different from the exponential, which indirectly indicated the presence of a defect.

The results of linear location analysis were as follows (Figure 27). On the lower boom belt, there were no sources of AE. Several sources were found on the column and the upper boom belt, which turned out to be noise, since they were localized near the winch attachment points, or were inactive. On the left and right struts, a linear location revealed 3 sources of AE on each, near which there were no potential sources of noise. Signals appeared at the stages of loading and unloading. The amplitude distributions presented above also make it possible to classify these sources as defects.

**Figure 27.** Results of acoustic emission location.

In addition, the spectrograms of the signals on the struts were analyzed, which made it possible to determine the arrival times of different frequency components (Figure 28). It was confirmed that the signals were emitted by impulsive sources and propagated along the walls of the object as Lamb waves. With the help of the spectrogram analysis, the distances between the AE sources and AE sensors were obtained, which coincided with the results of the usual linear location based on the analysis of the arrival times difference [27,28].

**Figure 28.** Dispersion curves for channel 29 (**a**); and for channel 30 (**b**).

At the points on the struts revealed as defective by the AE testing, an additional ultrasonic testing was carried out. On the left strut, 1 crack was found with the size of 20 mm, on the right—1 crack with the size 30 mm.

At present, the question of the SHM-system design for the testing of draglines is under consideration.

#### **4. Conclusions**


long lifetime. While testing the support rollers, the AE sources activity was registered on 2 shafts in the area of fillet. They presumably correspond to active fatigue cracks.

7. Dragline excavator: Work was carried out on AE monitoring of boom, column and struts of a walking dragline excavator. To remove noise, the use of a frequency range of 150–500 kHz and a threshold of 50 dBAE was most appropriate. With these settings, the AE activity acquires a cyclic character synchronized with the excavator operation cycle. By the results of attenuation measuring the recommended distance between the AE sensors is from 4.5 to 9 m. The AE data was collected for 2 days. Six sources of AE have been detected on the left and right struts. When performing ultrasound testing, 2 of them were classified as 20 and 30 mm cracks.

**Author Contributions:** S.A.E. managed the project and developed the software for the SHM-system. V.A.B. carried out the data acquisition and provided data filtering and clusterization. D.A.T. wrote the text of the article, participated in the data processing and verification of the obtained results. P.P.K. wrote the text of the article, and took part in the data analysis and verification of the results. V.V.B. carried out the data acquisition and the analytical research for all described applications. P.N.T. and A.L.A. created the original concept of the SHM-system and developed its hardware. V.G.K. took part in the design of the hardware of the SHM-system and developed the design of all units.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
