A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

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Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Sensors ◽

10.3390/s22020539 ◽

2022 ◽

Vol 22 (2) ◽

pp. 539

Author(s):

Farzin Piltan ◽

Rafia Nishat Toma ◽

Dongkoo Shon ◽

Kichang Im ◽

Hyun-Kyun Choi ◽

...

Keyword(s):

Machine Learning ◽

Acoustic Emission ◽

Support Vector Regression ◽

Support Vector ◽

Signal Estimation ◽

Digital Twin ◽

Crack Type ◽

Type Size ◽

Ae Signal ◽

Strict Feedback

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.

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Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

PLoS ONE ◽

10.1371/journal.pone.0261040 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261040

Author(s):

Zazilah May ◽

M. K. Alam ◽

Nazrul Anuar Nayan ◽

Noor A’in A. Rahman ◽

Muhammad Shazwan Mahmud

Keyword(s):

Machine Learning ◽

Acoustic Emission ◽

Feature Extraction ◽

Wavelet Packet ◽

Support Vector ◽

Learning Approach ◽

Severity Assessment ◽

Corrosion Detection ◽

Support Vector Classifier ◽

Machine Learning Approach

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

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Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System

Complexity ◽

10.1155/2020/6688912 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xiongwei Zhang ◽

Hager Saleh ◽

Eman M. G. Younis ◽

Radhya Sahal ◽

Abdelmgeid A. Ali

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Sentiment Analysis ◽

Real Time ◽

Machine Learning Algorithms ◽

Streaming Data ◽

Support Vector ◽

Real Time System ◽

Online Prediction ◽

Analysis Prediction

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus.

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Tool Wear Identification in Turning Titanium Alloy Based on SVM

Materials Science Forum ◽

10.4028/www.scientific.net/msf.800-801.446 ◽

2014 ◽

Vol 800-801 ◽

pp. 446-450 ◽

Cited By ~ 2

Author(s):

Zhi Rong Liao ◽

Sheng Ming Li ◽

Yong Lu ◽

Dong Gao

Keyword(s):

Support Vector Machine ◽

Acoustic Emission ◽

Titanium Alloy ◽

Tool Wear ◽

Cutting Tool ◽

Support Vector ◽

Test Set ◽

Tool Condition ◽

Good Classification ◽

Ae Signal

Titanium alloy is difficult cutting materials,the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features , SVM is trained and tested using these features ,Good classification results were achieved by using test set.

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A Method of Tools AE Signal Feature Extraction Based on Wavelet Packet Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.189 ◽

2011 ◽

Vol 474-476 ◽

pp. 189-194 ◽

Cited By ~ 1

Author(s):

Lan Shen Guo ◽

Nai Qiang Dong ◽

Wei Tian ◽

Fang Zhong Zhang ◽

Cai Xiao Li

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Tool Wear ◽

Feature Vector ◽

Wavelet Packet ◽

Signal Frequency ◽

Frequency Range ◽

Wavelet Packet Analysis ◽

Ae Signal ◽

The Will

The paper developed a method of tools AE signal feature extraction based on wavelet packet analysis, we used wavelet packet analysis to decompose acoustic emission signals for different frequency range of the signal components. The will occupy of the main energy of AE signal frequency range is extracted as the recognition feature vector of tool wear. The method has higher accuracy according to experiments.

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Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost

Symmetry ◽

10.3390/sym13020356 ◽

2021 ◽

Vol 13 (2) ◽

pp. 356

Author(s):

Shubham Mahajan ◽

Akshay Raina ◽

Xiao-Zhi Gao ◽

Amit Kant Pandit

Keyword(s):

Feature Extraction ◽

Transfer Learning ◽

Plant Species ◽

Species Recognition ◽

Morphological Features ◽

Support Vector ◽

Additional Advantage ◽

Adaptive Boosting ◽

Vast Number ◽

Axis Length

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.

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BioSignal modelling for prediction of cardiac diseases using intra group selection method

Intelligent Decision Technologies ◽

10.3233/idt-200058 ◽

2021 ◽

Vol 15 (1) ◽

pp. 151-160

Author(s):

Hemant P. Kasturiwale ◽

Sujata N. Kale

Keyword(s):

Machine Learning ◽

Nervous System ◽

Random Forest ◽

Normal Sinus Rhythm ◽

Heart Defects ◽

Support Vector ◽

Autonomous Nervous System ◽

Cardiac Diseases ◽

Vast Number ◽

Proposed Model

The Autonomous Nervous System (ANS) controls the nervous system and Heart Rate Variability (HRV) can be used as a diagnostic tool to diagnose heart defects. HRV can be classified into linear and nonlinear HRV indices which are used mostly to measure the efficiency of the model. For prediction of cardiac diseases, the selection and extraction features of machine learning model are effective. The available model used till date is based on HRV indices to predict the cardiac diseases accurately. The model could hardly throw light on specifics of indices, selection process and stability of the model. The proposed model is developed considering all facet electrocardiogram amplitude (ECG), frequency components, sampling frequency, extraction methods and acquisition techniques. The machine learning based model and its performance shall be tested using the standard BioSignal method, both on the data available and on the data obtained by the author. This is unique model developed by considering the vast number of mixtures sets and more than four complex cardiac classes. The statistical analysis is performed on a variety of databases such as MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyser using feature compatibility techniques. The classifiers are trained for prediction with approximately 40000 sets of parameters. The proposed model reaches an average accuracy of 97.87 percent and is sensitive and précised. The best features are chosen from the different HRV features that will be used for classification. The present model was checked under all possible subject scenarios, such as the raw database and the non-ECG signal. In this sense, robustness is defined not only by the specificity parameter, but also by other measuring output parameters. Support Vector Machine (SVM), K-nearest Neighbour (KNN), Ensemble Adaboost (EAB) with Random Forest (RF) are tested in a 5% higher precision band and a lower band configuration. The Random Forest has produced better results, and its robustness has been established.

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Headnote Prediction Using Machine Learning

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/5/7 ◽

2021 ◽

Vol 18 (5) ◽

Author(s):

Sarmad Mahar ◽

Sahar Zafar ◽

Kamran Nishat

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Active Learning ◽

Text Classification ◽

Extraction Methods ◽

Text Summarization ◽

Training Data ◽

Second Step ◽

Support Vector ◽

Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

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Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.36.68 ◽

2010 ◽

Vol 36 ◽

pp. 68-74

Author(s):

Chuan Jun Liao ◽

Shuang Fu Suo ◽

Wei Feng Huang

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Extraction Methods ◽

Mechanical Seal ◽

Mechanical Seals ◽

Feature Extraction Method ◽

Different Types ◽

Ae Signal ◽

Feature Information ◽

Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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