# Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission
## 4 Discussion  
Within the framework of this study, the possibility of the AE structural health monitoring of a dragline excavator was confirmed, and a method for interpreting diagnostic data was developed. This method allows to identify AE signal pulses which are responsible for a defect presence despite the background of intense noise.  
The distinctive feature of this study, compared with other approaches to AE monitoring of objects with a high noise level, is that a priori information was widely used in the construction of data processing algorithms. At the preliminary stage of the work, a detailed analysis of all AE monitoring technology components was performed: the AE source parameters, the acoustic path characteristics, and the noise features. The cumulative analysis of a priori information showed that the average noise level was several times higher than the average level of the AE pulse amplitudes. In addition, the intensity of the noise exceeds the AE activity by more than an order of magnitude. This is a significant obstacle for monitoring; however, the analysis of longtime noisy impact showed that its maximum level is about 80 dB. It also has an impulsive nature with quiet periods up to 40-45 dB, which is at least 50% of the time signal realization. Since the phase of the low noise level corresponds to the phase of loading the dragline bucket, it is possible to detect AE signal pulses against the background of noise.  
The AE diagnostics is based both on setting the threshold value of the amplitude discrimination below the noise level and on identification of the AE signal pulses by classification of the signal pulses and noise impulse components. The classification is done with the use of an acoustic waveguide parametric model. Classification criteria are specific and allow to remove up to 97% of noise data that differ in parameters from the AE signal pulses.  
The main advantage of the proposed approach is that the whole extended structure can be controlled due to the low discrimination threshold. At this time, AE monitoring systems are now commonly used only for the monitoring of the regions with the existing  
Figure 13: Results of AE event location on a right back leg of a dragline: (**a**) without filtering, (**b**) result of the amplitude filtering, (**c**) result of the spatial parametric filtering.  
defects. The reliability of the obtained results is confirmed by the fact that a defect in the weld of the dragline back leg was determined based on the AE monitoring data.  
The results obtained in the study have an applied character and a certain practical significance. They are useful for application in the control or monitoring of the dragline metal components during operation.  
Within the framework of the research, four dragline excavators \"E-Sh-20.90\", produced at the \"Uralmash\" plant from 1985 to 1994, were examined. For each of them, similar parameters of the noise process and close values of the acoustic waveguide parameters were observed. Despite the fact that defects were detected only in one of the monitored objects, the noise filtering procedure was effective in each case under consideration.