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The Application Reinforces The Signal
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By Ivan Panteleev Ivan Panteleev Scilit Preprints.org Google Scholar View Publications * , Aleksander Prokhorov Aleksander Prokhorov Scilit Preprints.org Google Scholar View Publications and Oleg Plekhov Oleg Plekhov Scilit Preprints.org
Submitted: 6 July 2021 / Revised: 4 August 2021 / Accepted: 10 August 2021 / Published: 16 August 2021
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This article presents a useful audio signal (related to loss accumulation) separation algorithm used to simulate the operation of an industrial rotating machine. Acoustic emission signals due to deformation and fracture were investigated using uniaxial tensile tests on laminated specimens cut in the grain and weft directions. The background signal is a unique mixture of acoustic pulses used to modulate the operation of industrial rotating equipment. The comparison of useful and noise signals enables us to develop two algorithms based on signal frequency filtering and its empirical methods of decomposition. These algorithms can be used to isolate useful AE pulses from the background of the entire observed signal intensity.
One of the main reasons for the ever-increasing demands of designs for automobiles, airplanes and aerospace engineering is to replace metal parts with various composites (carbon, carbon fiber and fiberglass, woven, knitted, stitched, etc.) [1 , 2, 3, 4, 5, 6]. However, the widespread use of composite parts of special barriers under continuous loading requires continuous monitoring of their performance and degradation (without sacrificing the performance of the structure during maintenance).
The most common non-destructive testing method used to solve such problems is the acoustic emission method [ 7 , 8 , 9 , 10 , 11 , 12 ]. This method records the strain waves generated by local deformations as they propagate and develop in the loaded material. Analysis of acoustic emission data provides information about the activity, location and, in some cases, type of acoustic emission sources [ 7 , 10 , 11 , 12 , 13 ].
Evaluation of constructions by EA raises the question of how the useful signal can be separated from different types of interference. Sources of background noise (continuous or discrete) are: friction of moving parts of the structure, technical flow and gas passage through pipes and channels, incoming air flow or water flow around important joints; electrical interference. etc. Isolation methods that help isolate discrete acoustic emission (AE) signals (due to material cracking) against continuous or discrete noise can be divided into three categories: spatial selection, parametric selection, and filtering.
Pdf) A Review Of The Application Acoustic Emission (ae) Incorporating Mechanical Approach To Monitor Reinforced Concrete (rc) Strengthened With Fiber Reinforced Polymer (frp) Properties Under Fracture
The location selection requires locating the acoustic emission source with an acceptable error, which is a complex problem to understand in the case of composite materials [ 14 , 15 , 16 , 17 ]. The parameter selection is effective when the maximum amplitude of the sound emission pulse is on average 5 dB higher than the amplitude of the noise component. Otherwise, the first filtering problem must be solved either to increase the signal-to-noise ratio or to separate the useful signal from noise such as time-space signals.
The most common filtering methods are windowed Fourier transform and discrete wavelet filtering. An important feature of the wavelet transform is the filtering of acoustic emission signals with a hard or soft threshold function (according to the noise model) [18, 19, 20]. Although the efficiency of wave conversion has already been proven, there are some drawbacks. The main drawback is the dependence of the data filtering and AI process on the primary channel .
Other non-stationary signal analysis and filtering methods are the empirical mode duction method and Prony filtering method [22, 23, 24, 25, 26, 27]. The empirical method combines the signals (through an iterative process) into a set of rotating signals (empirical methods) selected according to two requirements [23, 24, 25, 26, 27]. Unlike the wavelet transform, basis functions are derived directly from the analyzed signal and take into account its spatial characteristics. It is found that in some cases the time-frequency resolution of this method is better than that obtained by the wavelet transform. Signal filtering is done in the same way as in the wavelet transform case; In both cases, a specific threshold function is used to estimate the coefficients [23, 24].
The Prony filter method removes the signal by a linear combination of exponential-cosine waves while solving the residual function minimization problem. In order to filter the signal effectively, it is necessary to find the optimal distortion parameter. For this reason, the information obtained from different sources was analyzed, which makes it possible to exclude the noisy part of the signal from the reflection . In the reef. , the Prony method was successfully used in AE analysis to detect rolling bearing defects. According to , this method can be used in seismic and hydrocarbon field problems because it increases the resolution of short seismic signals and identifies areas with anomalous distribution (distortion) of seismic energy. The disadvantage of this method is the strong dependence of the synthesis process on the selected parameters.
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Different modes show audio filtering in the original recorded AE signal (the signal remains unchanged). The Kalman filter-based method is an effective signal processing technique widely used in radio navigation, guidance, and trajectory tracking applications [ 28 , 29 ]. Within column theory, various iterative data processing algorithms have been developed to recover the state of a given dynamical system based a priori on noisy and incomplete measurements. Therefore, the physical properties of AE signal propagation are considered. As for AE source location, these algorithms require less computational resources and show better accuracy compared to the wavelet transform [28, 29].
This method also includes another research method that has become popular recently. It deals with the use of artificial neural networks and the concept of deep learning for signal filtering. In , a supervised learning network was used to develop an algorithm to recover an AE signal with a signal-to-noise ratio of less than one. In addition, automatic associators (unsupervised learning) have been widely used in speech and image processing technologies [31, 32, 33, 34]. These algorithms are known to be able to provide a more accurate estimate of the signal-to-noise ratio compared to traditional filtering methods [ 32 , 33 , 34 ]. The advantage of these automatic classifiers is to classify the noise while preserving the real signal characteristics determined by the training set. However, it can be concluded that this principle of signal processing considers the study process as a column filtering technique.
In each case the choice of method for increasing the signal-to-noise ratio depends on the characteristics of the recorded signal and its spatio-temporal pattern of development.
This document pursues two objectives. First, there is the generation of acoustic emission signals caused by the cracking of carbon fiber reinforced plastic (CFRP) specimens. Another is to use an algorithm to separate the useful signal (corresponding to the brake) against the background signal to simulate the operation of industrial rotating equipment. Therefore, we present two algorithms designed to reduce different types of background noise.
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In this study, to determine the type of useful fracture-induced AE signals in composite materials, we performed a series of laboratory experiments in which carbon fiber-reinforced plastic specimens were loaded with different types and geometries.
Table 1 shows the information about the tested samples and loading parameters. The woven laminate specimens were subjected to fiber and warp directions, and the uniaxial CFRP specimens were subjected to uniaxial tensile stress. The specimens were for uniaxial tensile test
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