# Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission
## 2 Materials and Methods
### Filtering Method  
Conventionally, the detection of AE impulses is carried out by the threshold method, in accordance with AE, impulses are detected during the threshold crossing. This method is highly effective and convenient in a case when a noise has a stationary character and can be rejected with the nonlinear threshold filtering. However, in the case of nonstationary impulse noise, the use of the threshold method is not acceptable. According to the AE non-destructive testing method, the threshold is set in such a way that no more than one false hit detection is recorded in 100 s. According to the probability distribution of noise samples (Figure 8), the amplitude discrimination threshold should be set between 55 and 65 dB for different locations. Such an increase in the amplitude discrimination threshold leads to a significant increase in the probability of missing a defect. A preliminary analysis of the AE source shows that at a value of the amplitude discrimination threshold of 45 dB, the average emissivity of the material is about 105 AE hits per 1 mm of crack growth. However, it can be extrapolated that with an increase in the discrimination threshold from 45 to 60 dB, the number of AE hits drastically decreases by about 94% due to the exponential distribution of the AE pulse amplitudes (Figure 4b). The emissivity in this case will be on the order of 5-7 AE hits per 1 mm of crack growth. A special method was developed to increase the accuracy of crack detection in the dragline load-bearing metal structures. In accordance with this method, the amplitude discrimination level is set below the noise level to allow recording both the AE process and the noise technological process during the dragline operation. In this case, a type II error associated with the defect missing is minimized by increasing the probability of a type I error connected with false detection of a defect due to erroneous noise impulse location. Since the noise intensity is significantly higher than the AE hits count rate, special filtering is developed to increase the reliability of the testing. Filtering is effective and specific since it is based on the parametric model of AE impulse, representing a set of characteristic values of the AE parameters corresponding to a certain distance between the AE sensor and the defect. A spatial parametric filter is proposed for detecting AE impulses from nonstationary impulse noise. This filter excludes from the location schemes those AE events whose parameters are more consistent with the noise process and are not typical for AE impulses caused by a defect. Since the tested elements of the dragline are long pipelines (the length significantly exceeds the diameter), a linear circuit with an antenna formed by two sensors is used to determine the location of the defect. The filtering algorithm assumes that the waveform of AE impulse is determined primarily by the parameters of the acoustic waveguide. The acoustic emission impulses emitted by the AE source are stochastic and characterized by a short duration, on the order of nanoseconds. Since the propagation of the AE signals has a dispersive character, when propagating from the emission point to the AE sensor, the signal duration increases and reaches 100-1000 \(\upmu\)s at a distance about 10 meters, and the signal waveform is also determined mainly by the phases of different frequency components of the Lamb modes. In this case, the AE impulse, which is initially stochastic in nature, becomes principally deterministic, and its waveform and spectrum are mainly determined by the parameters of the waveguide. Based on the above, the following filtering method is applicable: for all located AE events, two \(RT\) parameters (\(RT_{1}\) for sensor 1 and \(RT_{2}\) for sensor 2) and two \(RA\) parameters (\(RA_{1}\) for sensor 1 and \(RA_{2}\) for sensor 2) are evaluated. Then, the correspondence between the values of the \(RA\), \(RT\) parameters and a defect distance are determined.