scholarly journals Investigating performance of transformer health index in machine learning application using dominant features

2021 ◽  
Vol 2128 (1) ◽  
pp. 012025
Author(s):  
Azlan Mohmad ◽  
M Ibrahim Shapiai ◽  
M Solehin Shamsudin ◽  
Mohd Azlan Abu ◽  
Amirah Abd Hamid
Keyword(s):  
Machine Learning ◽  
Dissipation Factor ◽  
Primary Objective ◽  
Health Index ◽  
Subsequent Investigation ◽  
Average Accuracy ◽  
Fold Type ◽  
Industry Practice ◽  
Artificial Neural Network Ann ◽  
Performance Validation

Abstract Transformer Health Index (HI) has become a standard tool for performing transformer health evaluations. Due to economic constraints, the recently published paper focuses on developing various techniques to identify the most dominant features for transformer HI prediction. However, the fundamental problems concerning their input features remain unresolved since most suggested features contradict industry practice. In this paper, the primary objective is to investigate the performance of the transformer HI by developing and utilizing only dominant features following the industry recommendation. The investigated dominant features in this paper using 1) CO2/CO ratio and 2) the Incipient fault for detecting temperature abnormalities, and 3) the Dissipation Factor (DF) for detecting oil contamination. The performance validation is carried out using various machine learning (ML) classifiers. Also, the performance of the ML model is validated based on 10-fold type cross-validation to avoid biases in the experiment. As a result, the proposed Artificial Neural Network (ANN) network utilizing the investigated dominant features following the industry practice has produced the highest average accuracy of 80.09% than others ML techniques as a classifier. Hence, additional studies to complement the investigated dominant features may be considered for the subsequent investigation.

scholarly journals Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning

2021 ◽  
Vol 2128 (1) ◽  
pp. 012024
Author(s):  
M Solehin Shamsudin ◽  
Fitri Yakub ◽  
M Ibrahim Shapiai ◽  
Azlan Mohmad ◽  
N Amirah Abd Hamid
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gas Analysis ◽  
Support Vector ◽  
Health Index ◽  
Testing Dataset ◽  
Random Neural Network ◽  
Periodic Maintenance ◽  
Artificial Neural Network Ann ◽  
Commercial Device

Abstract The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.

scholarly journals Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

2021 ◽  
Vol 10 (4) ◽  
pp. 570
Author(s):  
María A Callejon-Leblic ◽  
Ramon Moreno-Luna ◽  
Alfonso Del Cuvillo ◽  
Isabel M Reyes-Tejero ◽  
Miguel A Garcia-Villaran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Modelling Framework ◽  
Visual Analog Scales ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
Taste Disorders ◽  
Polymerase Chain ◽  
Fold Cross Validation ◽  
Validation Scheme ◽  
Control Study

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

2021 ◽  
Vol 11 (6) ◽  
pp. 2784
Author(s):  
Shahnaz TayebiHaghighi ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Rolling Element Bearing ◽  
Crack Identification ◽  
Original Signal ◽  
Signal Modeling ◽  
Residual Signal ◽  
Average Accuracy ◽  
Self Tuning ◽  
The Difference

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature

2020 ◽  
Author(s):  
R Pattnaik ◽  
K Sharma ◽  
K Alabarta ◽  
D Altamirano ◽  
M Chakraborty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Black Hole ◽  
Neutron Star ◽  
Binary Systems ◽  
Compact Object ◽  
X Ray ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
Low Mass ◽  
X Ray Binaries

Abstract Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87±13% in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

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