A Modified Leakage Localization Method Using Multilayer Perceptron Neural Networks in a Pressurized Gas Pipe

Pipelines leakage in power plant, petrochemical complexes, and refineries can lead to explosion, pollution, and severe physical damages, so precise and on time leak locating is very important. There are many techniques for detecting and locating the leakage. In this research, we represent the leak locating principle in pressurized gas pipelines by using acoustic emission theory. An algorithm for finding the location of continuous acoustic waves resulted from leakage is provided by MATLAB software. The used leak locating technique is a combination of wavelet transform, filtering, and cross-correlation methods. The resulted acoustic emission signals were analyzed into high and low frequencies by wavelet transform and available noises on them were omitted completely by filtering. Then de-noised acoustic emission signals were reconstructed. Time differences of de-noised waves were calculated precisely by using cross-correlation function. For studying the accuracy of used method, acoustic emission testing was done by continuous leakage source. The resulted signals of leakage were recorded by two acoustic sensors in two sides of leakage source, and time difference and leak location were calculated by using the algorithm. Several tests were done by changing sensor distance from leakage source and error percent of less than 3 % was resulted in leak locating that indicated high precision of used algorithm.

Carbon capture and storage (CCS) technology is considered to be one of the most promising options in dealing with the problem of global warming, where long distance transmission pipeline is often used for transporting captured CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to the storage site. A pipe leak detection and location system is established in the present work. An Acoustic emission (AE) signal occurs when CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> leak takes place. The signal propagates along the pipe and can be recorded by acoustic sensors mounted on the pipe. Experimental tests were performed to identify the leak location in the pipe through wavelet transform (WT) and empirical mode decomposition (EMD) of the AE signal. The relationship between the distance of two sensors and the leak location is investigated. Experimental results demonstrate that the maximum error in the estimation of the leak location is less than 5%.

The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized cross-correlation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location.

The monitoring and localization of defects and wire breaking of bridge cable are vital to the safe operation of the whole bridge. In this work, an innovative monitoring method that uses waveguides to draw out the steel wires at the end of the cable is proposed. This monitoring method can effectively capture the acoustic emission (AE) signal produced by defects and wire breaking of the cable. The transverse position of the wire breaking can also be accurately located so as to provide basis for the decision making of maintenance department. The pencil-lead breaks (PLB) test method is utilized to investigate the influence of the transition modes between the waveguide and the sensor and the connection modes between the waveguide and the cone on the AE signal. The PLB test and tensile test method are utilized to examine the AE signal characteristics of different sensors at the end of steel wires. Results showed that the cone transition between the waveguide and the sensor and the welding connection between waveguide and cone exhibit the minimum effect on AE waveform and parameters, such as amplitude, compared with that of the reference sensor without waveguide. In the tensile test, the transverse position of the wire breaking cannot be identified by analyzing the AE signals collected by each senor when the wire breaks, whereas it can be identified in the process of defects before the wire breaks.

The goal of this study is to classify Acoustic Emission (AE) signals based on their characteristics into several fracture types. AE method allows us to classify the damage process of CFRP with machine learning. However, unsupervised learning was often applied for classification which required manually matching the resulting clusters to specific damage types. In contrast, supervised learning to AE signals can automatically determine the damage types. A supervised learning technique uses many AE signals corresponding to each damage mode (supervised signals) in advance. In this study, supervised signals for Mode I and Mode II delamination were extracted from AE signals generated during Double Cantilever Beam (DCB) and Short Beam (SB) tests, respectively. While these delamination modes are dominant in each test, AE signals from other damage types, such as matrix cracking and fiber breakage, are also present. To account for this, additional AE data were obtained from tensile and three-point bending tests, where such damage is dominant. Finally, principal component analysis (PCA) was applied to visualize the distribution of AE signals. The results showed that AE signals from DCB and SB formed distinct clusters, while some overlapped with clusters from tensile and three-point bending signals. This suggests that by excluding overlapping clusters, it is possible to extract features that are more specific to Mode I and Mode II delamination.

A novel machine learning-based model is introduced in this research paper to forecast tool wear using acoustic emission (AE) signals. Adaptive boosting (AdaBoost) and a sophisticated feature engineering strategy are employed by the model to enhance the precision of its predictions. The proposed model, Machine Learning Tool Wear Prediction (MLTWP), analyzes AE signals generated during machining operations to distinguish between healthy and worn-out tool conditions with remarkable accuracy. The crux of our approach consists of meticulously eliminating and enhancing the temporal and spectral characteristics of the AE signals. We employ the Kolmogorov-Smirnov test to identify the most valuable classification features. We implemented AdaBoost with the objective of progressively enhancing a set of weak classifiers’ ability to identify instances that were incorrectly classified in previous iterations. Utilizing this method increases the model’s sensitivity to minute variations in tool wear conditions and its overall classification precision. The MLTWP model underwent extensive testing on a benchmark data set comprising 25,304 AE signal records from cutting mill tools, using a training tool split of 9,989 worn (positive) and 8,990 benign (negative) instances. The results of our experiments, validated through four-fold cross-validation, indicate that the MLTWP model exhibits superior performance compared to the existing Tool Wear Prediction using Acoustic Emission Signals (TWPAE) model. To provide greater specificity, the MLTWP exhibited the following metrics on average: precision (92.2%), specificity (91.38%), sensitivity (90.42%), accuracy (90.9%), and MCC (81.72%). The fact that these metrics exhibit significant improvement over those of the TWPAE model demonstrates that our method of feature engineering and adaptive boosting is effective at precisely predicting tool wear. This research not only advances the existing understanding of tool wear prediction but also establishes a robust framework for the implementation of machine learning in manufacturing predictive maintenance.

A multisignal nanosecond synchronous acquisition system to measure acoustic emission (AE) and electromagnetic radiation (EMR) generated during the process of loading and failure of coal and rock samples is established. The correlation between the energy of the AE and EMR signals and the loading stress of outburst coal-rock samples was studied, and the characteristics of the AE and EMR signals during the process of loading and fracturing the outburst coal and rock samples were analyzed. The results show that (1) before the failure of the outburst coal and rock samples, the fluctuation of the AE and EMR signals is the largest, with the same rising and falling trend, and the intensity is not strictly positively correlated, with the phenomenon of low EMR when the AE intensity is high; (2) the EMR and AE deviation degree and frequency exhibit a good response to coal and rock fracturing. The correlation between EMR and stress drop is stronger than that of AE, and the AE signal is richer than the EMR signal. The results show that it is feasible to develop combined AE and EMR early warning technology to improve the early forecasting accuracy of coal and gas outbursts.

Strain localization peculiarities and distribution of acoustic emission sources in rock samples tested by uniaxial compression and exposed to electric pulses

Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy

This paper is focused on the application of acoustic emission (AE) based monitoring systems to grinding processes. To develop the AE signal model for the process monitoring, a comparison between the AE and power signals is presented. The AE model is developed according to a traditional grinding force model. Based on the developed model, experiment indicates the AE signal is similar to the power of spindle and verifies the proposed AE model can be used to predict the AE root meam square (RMS) signal for monitoring the grinding process.

The research about online monitoring and leakage automatic location of water distribution networks (WDN) has a wide range of applications that include water resource protection, monitoring, and allocation. Variational mode decomposition (VMD) and cross-correlation (CC) based leakage location is a popular and effective method in WDN. However, the value of K intrinsic mode functions (IMFs) based on VMD decomposition needs to be determined artificially, which affects the separation effect of signal frequency band characteristics directly. Hence, this work proposes an adaptive method to determine the parameter K of leakage vibration signal’s IMFs, which will be applied to automatic leakage location in WDN. Firstly, the number of saddle points in the frequency domain envelope of the sampled signal in different step sizes is calculated. The parameter K is determined according to the curvature change of the number of saddle points and the sampled signal. Finally, the selective IMFs are reconstituted into a new signal, which can determine a leak position using CC based time-delay estimation (TDE). To verify the effectiveness of the proposed algorithm, the different methods based on EMD and Fast ICA are compared. The experimental results demonstrate that the proposed parameter K value adaptive VMD (KVA-VMD) decomposition method is more suitable for leakage location in WDN.

Characterization of piezoelectric wafer active sensor for acoustic emission sensing

Objective: This study aimed to implement a model for detecting and locating leaks in water distribution networks, using artificial intelligence (artificial neural networks). Background: Traditional leak detection methods, such as visual inspections and flow and pressure analysis, are being progressively complemented by solutions based on smart sensors and machine learning techniques. Method: Using the method of characteristics to solve the momentum and continuity equations, along with nodal equations, a hydraulic transient simulator was created for water distribution networks with leaks. For the leak simulation, the orifice law (Torricelli's Law) was used. Results and Discussion: The developed model successfully located the leak with an error of approximately 5.65%, corresponding to a distance of approximately 61.22 m for the studied network. Research Implications: Deteriorated infrastructure in water distribution systems can lead to leakage losses, reduced water transport capacity, component failures, increased maintenance and operating costs, frequent system interruptions, and decreased reliability. Thus, the exploration of rapid leak detection and location methods has increased, aiming to mitigate environmental impacts and the losses of natural and financial resources. Originality/Value: The methodological distinction lies in using pressure wave travel times as input data, rather than absolute pressure values. This approach eliminates the need for detailed hydraulic network calibration, reduces sensitivity to operational noise, and broadens the practical applicability of the model.

An Acoustic Emission (AE) signal processing and analysis system is developed for process and condition monitoring of Dense Medium Cyclones (DMCs), key units in coal preparation plants. This paper describes the use of a combination of statistical signal processing, multivariate statistical analysis and pattern classification methods to formulate robust soft-sensor predictive models for DMC process variables and wear state based on passive contact AE signals. Power spectral density and statistical moment ‘feature’ variables are extracted from AE signals. Principal Component Analysis (PCA) is used to evaluate the sensitivity of the AE ‘features’ to process variables and wear state. Principal Component Regression (PCR) and Partial Least Squares (PLS) are used to quantitatively model process variables. Hierarchical clustering is used to qualitatively model wear state and process variables. The AE hardware, signal processing and soft-sensor modelling system can be used for process and condition monitoring of other mineral processing equipment.

The new method for discrete fractional cosine transform is proposed, and it was introduced in acoustic emission signal enhancement. According to the characteristic of rub-impact acoustic emission (AE) signal from rotating machinery, two constructions are proposed to enhance AE signal, which based on three cycles discrete fractional cosine transform (FDCT3), and modified transform (MFDCT3). The experiment date was rub impact AE signals sampled from rotating test stand, Gaussian white noise and non-stationary noise were added to simulate the real AE signal which polluted seriously by noise. Then, FDCT3 and MFDCT3 were used into de-noising and AE recognition.The result shown that FDCT3 and MFDCT3 are effective at the de-noising, and those performance is better than DCT.