scholarly journals Ensemble Learning Approach for the Prediction of Quantitative Rock Damage Using Various Acoustic Emission Parameters

2021 ◽  
Vol 11 (9) ◽  
pp. 4008
Author(s):  
Hang-Lo Lee ◽  
Jin-Seop Kim ◽  
Chang-Ho Hong ◽  
Dong-Keun Cho
Keyword(s):  
Acoustic Emission ◽  
Difficult Problem ◽  
Radioactive Waste Disposal ◽  
Parameter Analysis ◽  
Support Vector ◽  
Uniaxial Compression Test ◽  
Rock Damage ◽  
Initiation Frequency ◽  
Combined Features ◽  
Future Work

Monitoring rock damage subjected to cracks is an important stage in underground spaces such as radioactive waste disposal repository, civil tunnel, and mining industries. Acoustic emission (AE) technique is one of the methods for monitoring rock damage and has been used by many researchers. To increase the accuracy of the evaluation and prediction of rock damage, it is required to consider various AE parameters, but this work is a difficult problem due to the complexity of the relationship between several AE parameters and rock damage. The purpose of this study is to propose a machine learning (ML)-based prediction model of the quantitative rock damage taking into account of combined features between several AE parameters. To achieve the goal, 10 granite samples from KAERI (Korea Atomic Energy Research Institute) in Daejeon were prepared, and a uniaxial compression test was conducted. To construct a model, random forest (RF) was employed and compared with support vector regression (SVR). The result showed that the generalization performance of RF is higher than that of SVRRBF. The R2, RMSE, and MAPE of the RF for testing data are 0.989, 0.032, and 0.014, respectively, which are acceptable results for application in laboratory scale. As a complementary work, parameter analysis was conducted by means of the Shapley additive explanations (SHAP) for model interpretability. It was confirmed that the cumulative absolute energy and initiation frequency were selected as the main parameter in both high and low-level degrees of the damage. This study suggests the possibility of extension to in-situ application, as subsequent research. Additionally, it provides information that the RF algorithm is a suitable technique and which parameters should be considered for predicting the degree of damage. In future work, we will extend the research to the engineering scale and consider the attenuation characteristics of rocks for practical application.

scholarly journals Numerical Study of Rock Fragmentation Process and Acoustic Emission by FDEM Based on Heterogeneous Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qi Liu ◽  
Penghai Deng
Keyword(s):  
Acoustic Emission ◽  
Mechanical Response ◽  
Numerical Study ◽  
High Energy ◽  
Uniaxial Compression Test ◽  
Rock Damage ◽  
Heterogeneous Model ◽  
Damage And Fracture ◽  
Heterogeneous Rock ◽  
Rock Model

Rock has the characteristics of natural heterogeneity and discontinuity. Its failure phenomenon induced by external force involves complex processes, including the microcrack initiation, propagation, coalescence, and the macrocrack formation. In this study, the Weibull random distribution based on the rock microstructure characteristics is introduced into the combined finite-discrete element method (FDEM) to establish the heterogeneous rock model, and the mechanical response and damage evolution of rock samples in uniaxial compression test are simulated. The results show that FDEM simulation with loaded heterogeneous rock model can reflect the progressive development of rock damage, fracture, and acoustic emission (AE) activity in real rock well. Meanwhile, the statistical analysis indicates that the number and energy evolution of AE events with different fracture modes in the model are consistent with the macroscopic failure mode of rock. The change of b-value also agrees with the increasing trend of high-energy events in the loading process. This method provides a new tool for the analysis of rock damage and fracture evolution.

A new combined transient extraction method coupled with WO3 gas sensors for polluting gases classification

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri
Keyword(s):  
Feature Extraction ◽  
Gas Sensors ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Support Vector ◽  
Classification Rate ◽  
Content Type ◽  
Feature Extraction Method ◽  
Combined Features ◽  
Discrimination Capacity

Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

Optimized intelligent algorithm for classifying cloud particles recorded by a Cloud Particle Imager

2021 ◽  
Author(s):  
Zepei Wu ◽  
Shuo Liu ◽  
Delong Zhao ◽  
Ling Yang ◽  
Zixin Xu ◽  
...  
Keyword(s):  
Processing Speed ◽  
Cloud Microphysics ◽  
Parameter Analysis ◽  
Ice Crystals ◽  
Support Vector ◽  
Svm Classifier ◽  
Ice Crystal ◽  
Cloud Particle ◽  
Average Accuracy ◽  
Cloud Particles

AbstractCloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis, to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on ten types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2,000 images/s. In actual application, the algorithm should consider the processing speed, because the images are in the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2,000 images/s. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

Classification of journal bearing friction states based on acoustic emission signals

2018 ◽  
Vol 85 (6) ◽  
pp. 434-442 ◽  
Author(s):  
Noushin Mokhtari ◽  
Clemens Gühmann
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Journal Bearing ◽  
Prediction Method ◽  
Support Vector ◽  
Mechatronic Systems ◽  
Prediction Time ◽  
Ae Signals ◽  
Bearing Friction

Abstract For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. To increase the sensitivity of the extracted features the AE signals were transformed to the frequency-time-domain using continuous wavelet transform (CWT). Significant frequency bands are determined to separate different friction states more effective. A support vector machine (SVM) is used to classify the signals into three different friction classes. In the end the idea for an RUL prediction method by using the already determined information is given and explained.

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Intelligent Fault Diagnosis of Synchromesh Gearbox Using Fusion of Vibration and Acoustic Emission Signals for Performance Enhancement

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
T Praveenkumar ◽  
M Saimurugan ◽  
K I Ramachandran
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Support Vector ◽  
Feature Level Fusion ◽  
Sensor Signals ◽  
Early Fault Detection ◽  
Ae Signals ◽  
Level Fusion

Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.

Classification of Rusty and Non-Rusty Images

2020 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Mridu Sahu ◽  
Tushar Jani ◽  
Maski Saijahnavi ◽  
Amrit Kumar ◽  
Upendra Chaurasiya ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Support Vector ◽  
Suitable Model ◽  
Online Databases ◽  
Perlin Noise ◽  
Combined Features ◽  
Different Parts

Rust detection is necessary for proper working and maintenance of machines for security purposes. Images are one of the suggested platforms for rust detection in which rust can be detected even though the human can't reach to the area. However, there are a lack of online databases available that can provide a sizable dataset to identify the most suitable model that can be used further. This paper provides a data augmentation technique by using Perlin noise, and further, the generated images are tested on standard features (i.e., statistical values, entropy, along with SIFT and SURF methods). The two most generalized classifiers, naïve Bayes and support vector machine, are identified and tested to obtain the performance of classification of rusty and non-rusty images. The support vector machine provides better classification accuracy, which also suggests that that the combined features of statistics, SIFT, and SURF are able to differentiate the images. Hence, it can be further used to detect the rust in different parts of machines.

Artificial intelligence exploitation in facility management using deep learning

2020 ◽  
Vol 20 (4) ◽  
pp. 609-624
Author(s):  
Mohamed Marzouk ◽  
Mohamed Zaher
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Life Cycle Cost ◽  
Facility Management ◽  
Support Vector ◽  
Maintenance Costs ◽  
Content Type ◽  
Protection Systems ◽  
Future Work

Purpose This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team. Design/methodology/approach This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance. Findings The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system. Research limitations/implications The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work. Originality/value This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

scholarly journals Determination of the excitation origin in the ventricles from the ECG using support vector regression

2017 ◽  
Vol 3 (2) ◽  
pp. 257-260
Author(s):  
Nicolas Pilia ◽  
Christian Ritter ◽  
Danila Potyagaylo ◽  
Walther H. W. Schulze ◽  
Olaf Dössel ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Functional Relation ◽  
Support Vector ◽  
Transmembrane Potentials ◽  
Surface Ecg ◽  
Machine Learning Approach ◽  
Ill Posed ◽  
Ectopic Beats ◽  
Forward Calculation ◽  
Future Work

AbstractA common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

scholarly journals An Experimental Investigation of Moisture-Induced Softening Mechanism of Marble Based on Quantitative Analysis of Acoustic Emission Waveforms

2019 ◽  
Vol 9 (3) ◽  
pp. 446
Author(s):  
Huang Yiming ◽  
Deng Jianhui ◽  
Zhu Jun
Keyword(s):  
Acoustic Emission ◽  
Dominant Frequency ◽  
Uniaxial Compression Test ◽  
Test Results ◽  
Frequency Bands ◽  
Softening Mechanism ◽  
Final Failure ◽  
Shear Failures ◽  
Microcosmic Mechanism ◽  
Ae Signals

The decrease of strength after saturation of rocks is known as moisture-induced softening. To date, there are numerous studies on the mechanism of moisture-induced softening of different rocks. However, due to a lack of effective observational methods, the microcosmic mechanism of moisture-induced softening still needs to be understood. We collected and processed acoustic emission (AE) signals during the uniaxial compression test of marble specimens. The results of spectral and statistical analysis show that two dominant frequency bands of AE waveforms exist regardless of the specimen’s water content. Additionally, for the AE signals from the saturated specimens, the ranges of the low and high frequency bands are wider than dried rock samples. Besides, since the tensile and shear failures in the rock release low and high dominant frequency AE signals, respectively, the test results of this paper show that micro-shear and micro-tensile failures dominate the final failure of dried and saturated rocks, respectively.

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