Records with Keyword: Machine Learning
Showing records 1 to 25 of 700. [First] Page: 1 2 3 4 5 Last
Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass
Chengcheng Liu, Xuandong Wang, Weidong Cai, Yazhou He, Hang Su
February 19, 2024 (v1)
Keywords: glass-forming ability, Machine Learning, metallic glass, optimal design
The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to... [more]
Exploring Bayesian Optimization for Photocatalytic Reduction of CO2
Yutao Zhang, Xilin Yang, Chengwei Zhang, Zhihui Zhang, An Su, Yuan-Bin She
January 12, 2024 (v1)
Subject: Optimization
Keywords: Bayesian optimization, design of experiment, Machine Learning, photocatalytic reduction, reaction optimization
The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO2 reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO2, partial pressure of H2O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which we... [more]
A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning
Huayang Li, Qiang Tan, Jingen Deng, Baohong Dong, Bojia Li, Jinlong Guo, Shuiliang Zhang, Weizheng Bai
November 30, 2023 (v1)
Subject: Other
Keywords: empirical models, Extra Trees, KNN, LightGBM, Machine Learning, overpressure, pore pressure prediction, Random Forest, well logs
In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density−sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast... [more]
A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network
Youchun Pi, Yun Tan, Amir-Mohammad Golmohammadi, Yujing Guo, Yanfeng Xiao, Yan Chen
November 30, 2023 (v1)
Keywords: Fault Detection, generalized neural network, industrial systems, Machine Learning, sand cat swarm optimization
With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, thereby reducing production interruptions and maintenance costs, improving production efficiency, and enhancing equipment reliability. Machine learning techniques have proven highly effective for fault detection in modern production processes. Among numerous machine learning algorithms, the generalized neural network stands out due to its simplicity, effectiveness, and applicability to various fault warning scenarios. However, the increasing complexity of systems and equipment presents significant challenges to the generalized neural network. In real-world scenarios, it suffers from drawbacks such as difficulties in determining parameters and getting trapped in local opt... [more]
Green Techniques for Detecting Microplastics in Marine with Emphasis on FTIR and NIR Spectroscopy—Short Review
Vlatka Mikulec, Petra Adamović, Želimira Cvetković, Martina Ivešić, Jasenka Gajdoš Kljusurić
September 21, 2023 (v1)
Keywords: ecotoxicological testing, health impact, Machine Learning, marine pollution, microplastics, microplastics analysis, novel methods
The amount of microplastics (MPs) present in marine ecosystems are a growing concern, with potential impacts on human health because they are associated with an increase in the ecotoxicity of certain foods, such as fish. As a result, there has been a growing interest in developing effective methods for the analysis of MPs in marine waters. Traditional methods for MP analysis involve visual inspection and manual sorting, which can be time-consuming and subject to human error. However, novel methods have been developed that offer more efficient and accurate analyses. One such method is based on spectroscopy, such as Fourier transform infrared spectroscopy (FTIR). Another method involves the use of fluorescent dyes, which can selectively bind to microplastics and allow for their detection under UV light. Additionally, machine learning approaches have been developed to analyze large volumes of water samples for MP detection and classification. These methods involve the use of specialized a... [more]
Counting Abalone with High Precision Using YOLOv3 and DeepSORT
Duncan Kibet, Jong-Ho Shin
September 21, 2023 (v1)
Keywords: abalone, abalone counting, abalone detection, deep learning, Machine Learning
In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry.... [more]
Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review
Zeyu Fan, Ziju He, Wenjun Miao, Rongrong Huang
September 20, 2023 (v1)
Subject: Biosystems
Keywords: classification algorithm, gastric cancer, Machine Learning, predictive factors, risk prediction model
The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastri... [more]
An Interpretable Predictive Model for Health Aspects of Solvents via Rough Set Theory
Wey Ying Hoo, Jecksin Ooi, Nishanth Gopalakrishnan Chemmangattuvalappil, Jia Wen Chong, Chun Hsion Lim, Mario Richard Eden
September 20, 2023 (v1)
Keywords: health indices, Machine Learning, organic solvents, rough set theory, rough set-based machine learning
This paper presents a machine learning (ML) approach to predict the potential health issues of solvents by uncovering the hidden relationship between substances and toxicity. Solvent selection is a crucial step in industrial processes. However, prolonged exposure to solvents has been found to pose significant risks to human health. To mitigate these hazards, it is crucial to develop a predictive model for health performance by identifying the contributing factors to solvent toxicity. This research aims to develop a predictive model for health issues related to solvent toxicity. Among various algorithms in ML, Rough Set Machine Learning (RSML) was chosen for this work due to its interpretable nature of the generated models. The models have been developed through data collection on the toxicity of various organic solvents, the construction of predictive models with decision rules, and model verification. The results reveal correlations between solvent toxicity and the Balaban index, vale... [more]
Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification
Ruhao Liu, Lei Zhang, Xinrui Wang, Xuejuan Zhang, Xingzhou Liu, Xin He, Xiaoming Zhao, Dianshi Xiao, Zheng Cao
August 3, 2023 (v1)
Keywords: lithofacies classification, Machine Learning, shale
Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and... [more]
Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends
Esteban Lopez-Ramirez, Sandra Lopez-Zamora, Salvador Escobedo, Hugo de Lasa
August 3, 2023 (v1)
Keywords: Artificial Neural Networks, hydrocarbon/water blends, Machine Learning, vapour-liquid-liquid equilibrium
Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has... [more]
Determination of Soil Agricultural Aptitude for Sugar Cane Production in Vertisols with Machine Learning
Ofelia Landeta-Escamilla, Alejandro Alvarado-Lassman, Oscar Osvaldo Sandoval-González, José de Jesús Agustín Flores-Cuautle, Erik Samuel Rosas-Mendoza, Albino Martínez-Sibaja, Norma Alejandra Vallejo Cantú, Juan Manuel Méndez Contreras
August 3, 2023 (v1)
Keywords: land use, Machine Learning, soil agricultural aptitude, sugar cane, vertisols
Sugarcane is one of the main agro-industrial products consumed worldwide, and, therefore, the use of suitable soils is a key factor to maximize its production. As a result, the need to evaluate soil matrices, including many physical, chemical, and biological parameters, to determine the soil’s aptitude for growing food crops increases. Machine learning techniques were used to perform an in-depth analysis of the physicochemical indicators of vertisol-type soils used in sugarcane production. The importance of the relationship between each of the indicators was studied. Furthermore, and the main objective of the present work, was the determination of the minimum number of the most important physicochemical indicators necessary to evaluate the agricultural suitability of the soils, with a view to reducing the number of analyses in terms of physicochemical indicators required for the evaluation. The results obtained relating to the estimation of agricultural capability using different numbe... [more]
Development of a Lux Meter for the Identification of Liquids in Post-Consumer Polyethylene Terephthalate Bottles for Collection Centers in Mexico
L. A. Ángeles-Hurtado, Juvenal Rodríguez-Reséndiz, Hilda Romero Zepeda, Hugo Torres-Salinas, José R. García-Martínez, Silvia Patricia Salas-Aguilar
August 3, 2023 (v1)
Keywords: ANOVA, automation, classification, illuminance, lux meter, Machine Learning, municipal solid waste, PET, recycling
This article aims to enhance technological advancements in the classification of polyethylene terephthalate (PET) bottle plastic, positively impacting sustainable development and providing effective solutions for collection centers (CC) in Mexico. Three experimental designs and machine learning tools for data processing were developed. The experiments considered three factors: bottle size, liquid volume, and bottle labels. The first experiment focused on determining the sensor distance from post-consumer PET bottles. The second experiment aimed to evaluate the sensor’s detection ability with varying liquid levels, while the third experiment assessed its detection capability for bottle labels. A digital lux meter integrated with a microcontroller was developed to monitor illuminance in post-consumer PET bottles containing liquid as they moved through a conveyor belt at an average rate of three bottles per second. The implemented methodology successfully detected liquids inside transpare... [more]
Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification
Olutomilayo Olayemi Petinrin, Faisal Saeed, Naomie Salim, Muhammad Toseef, Zhe Liu, Ibukun Omotayo Muyide
August 2, 2023 (v1)
Subject: Biosystems
Keywords: cancer classification, gene expression, Machine Learning, microarray data, sampling methods
Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validatio... [more]
Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit
Yuyang Mu, Juan Carlos Salas
July 7, 2023 (v1)
Keywords: cluster analysis, copper deposit, geometallurgy, Machine Learning, unsupervised learning
Geometallurgy integrates aspects of geology, metallurgy, and mine planning in order to improve decision making in mining schedules. A geometallurgical model is a 3D space that is typically synthesized from early-stage small-scale samples and is composed of several metallurgical units, or domains. This work explores the synthesis of a geometallurgical model for a copper deposit using a purely data-driven unsupervised approach. To this end, a dataset of 1112 drill samples is used, which are clustered using different methods, namely, k-means, hierarchical clustering (AGG), self-organizing maps (SOM), and DBSCAN. Two cluster validity indices (Silhouette and Calinski−Harabasz) are used to select the final model. To validate the potential of the proposed approach, a simulated economic evaluation is conducted. Results demonstrate that k-means exhibits a better performance in terms of modeling and that using the obtained geometallurgical model for mining scheduling increases the project’s Net... [more]
Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review
Vinay Kumar Pandey, Shivangi Srivastava, Kshirod Kumar Dash, Rahul Singh, Shaikh Ayaz Mukarram, Béla Kovács, Endre Harsányi
July 7, 2023 (v1)
Keywords: Artificial Intelligence, fruit preservation, Machine Learning, nanotechnology
Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more busines... [more]
Formulation of Nucleic Acids by Encapsulation in Lipid Nanoparticles for Continuous Production of mRNA
Alina Hengelbrock, Axel Schmidt, Jochen Strube
July 7, 2023 (v1)
Keywords: autonomous operation, continuous biomanufacturing, digital twins, in vitro transcription, lipid nanoparticles, Machine Learning, mRNA vaccine manufacturing
The development and optimization of lipid nanoparticle (LNP) formulations through hydrodynamic mixing is critical for ensuring the efficient and cost-effective supply of vaccines. Continuous LNP formation through microfluidic mixing can overcome manufacturing bottlenecks and enable the production of nucleic acid vaccines and therapeutics. Predictive process models developed within a QbD Biopharma 4.0 approach can ensure the quality and consistency of the manufacturing process. This study highlights the importance of continuous LNP formation through microfluidic mixing in ensuring high-quality, in-specification production. Both empty and nucleic acid-loaded LNPs are characterized, followed by a TFF/buffer exchange to obtain process parameters for the envisioned continuous SPTFF. It is shown that LNP generation by pipetting leads to a less preferable product when compared to continuous mixing due to the heterogeneity and large particle size of the resulting LNPs (86−104 nm). Particle siz... [more]
Digital Twin Implementation for Manufacturing of Adjuvants
Poonam Phalak, Emanuele Tomba, Philippe Jehoulet, André Kapitan-Gnimdu, Pablo Martin Soladana, Loredana Vagaggini, Maxime Brochier, Ben Stevens, Thomas Peel, Laurent Strodiot, Sandrine Dessoy
July 7, 2023 (v1)
Keywords: adjuvant particles, digital twins, Machine Learning, process analytical technology, process modeling, quality by design
Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances... [more]
Study on the Optimal Double-Layer Electrode for a Non-Aqueous Vanadium-Iron Redox Flow Battery Using a Machine Learning Model Coupled with Genetic Algorithm
Qiang Ma, Wenxuan Fu, Jinhua Xu, Zhiqiang Wang, Qian Xu
June 9, 2023 (v1)
Keywords: 3D finite-element numerical simulation, artificial neural network, DES electrolyte, Genetic Algorithm, gradient porous electrode, Machine Learning, operational performance, redox flow battery, vanadium-iron
To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode’s architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB’s performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled wi... [more]
Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems
Mohammad Hijjawi, Mohammad Alshinwan, Osama A. Khashan, Marah Alshdaifat, Waref Almanaseer, Waleed Alomoush, Harish Garg, Laith Abualigah
June 7, 2023 (v1)
Keywords: AOA, cuckoo search, Machine Learning, Truss bar, welded beam
Several metaheuristic algorithms have been implemented to solve global optimization issues. Nevertheless, these approaches require more enhancement to strike a suitable harmony between exploration and exploitation. Consequently, this paper proposes improving the arithmetic optimization algorithm (AOA) to solve engineering optimization issues based on the cuckoo search algorithm called AOACS. The developed approach uses cuckoo search algorithm operators to improve the ability of the exploitation operations of AOA. AOACS enhances the convergence ratio of the presented technique to find the optimum solution. The performance of the AOACS is examined using 23 benchmark functions and CEC-2019 functions to show the ability of the proposed work to solve different numerical optimization problems. The proposed AOACS is evaluated using four engineering design problems: the welded beam, the three-bar truss, the stepped cantilever beam, and the speed reducer design. Finally, the results of the prop... [more]
Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites
Gohar Shoukat, Guillaume Michel, Mark Coughlan, Abdollah Malekjafarian, Indrasenan Thusyanthan, Cian Desmond, Vikram Pakrashi
May 23, 2023 (v1)
Keywords: ANNs, CPT, geotechnics, Machine Learning, Renewable and Sustainable Energy
The initial design phase for offshore wind farms does not require complete geotechnical mapping and individual cone penetration testing (CPT) for each expected turbine location. Instead, background information from open source studies and previous historic records for geology and seismic data are typically used at this early stage to develop a preliminary ground model. This study focuses specifically on the interpolation and extrapolation of cone penetration test (CPT) data. A detailed methodology is presented for the process of using a limited number of CPTs to characterise the geotechnical behavior of an offshore site using artificial neural networks. In the presented study, the optimised neural network achieved a predictive error of 0.067. Accuracy is greatest at depths of less than 10 m. The pitfalls of using machine learning for geospatial interpolation are explained and discussed.
Model for Predicting CO2 Adsorption in Coal Left in Goaf Based on Backpropagation Neural Network
Fei Gao, Peng Wang, Dapeng Wang, Yulong Yang, Xun Zhang, Gang Bai
May 23, 2023 (v1)
Keywords: CO2 sequestration, Coal, influence factors, Machine Learning, pore structure
Injecting power plant flue gas into a goaf stores CO2 in the flue gas and effectively prevents the spontaneous combustion of the coal remaining in the goaf. Here, we investigated the adsorption behavior of three types of coal at normal temperature and pressure using a self-developed adsorption experimental device. We used a specific surface area and porosity analyzer to study the effects of pore structure, mineral content, and moisture content on CO2 adsorption in coal. Based on the experimental data, we designed a multifactor CO2 adsorption prediction model based on a backpropagation (BP) neural network. The results indicated that the pore size of most micropores in coal was in the range of 0.5−0.7 and 0.8−0.9 nm. The specific surface area and pore volume were positively correlated with the CO2-saturated adsorption capacity, whereas the mean pore diameter, mineral content, and moisture content were inversely associated with the CO2-saturated adsorption amount. The accuracy of the mult... [more]
Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms
Fateme Dinmohammadi, Yuxuan Han, Mahmood Shafiee
May 23, 2023 (v1)
Keywords: energy consumption, Machine Learning, Net-Zero, prediction, residential building
The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing... [more]
Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA
Mehran Tahir, Stefan Tenbohlen
May 23, 2023 (v1)
Keywords: condition assessment, decision tree (DT), frequency response analysis (FRA), Machine Learning, numerical indices, power transformer, random forest (RF)
At present, the condition assessment of transformer winding based on frequency response analysis (FRA) measurements demands skilled personnel. Despite many research efforts in the last decade, there is still no definitive methodology for the interpretation and condition assessment of transformer winding based on FRA results, and this is a major challenge for the industrial application of the FRA method. To overcome this challenge, this paper proposes a transformer condition assessment (TCA) algorithm, which is based on numerical indices, and a supervised machine learning technique to develop a method for the automatic interpretation of FRA results. For this purpose, random forest (RF) classifiers were developed for the first time to identify the condition of transformer winding and classify different faults in the transformer windings. Mainly, six common states of the transformer were classified in this research, i.e., healthy transformer, healthy transformer with saturated core, mecha... [more]
Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types
Abhimanyu Kapuria, Daniel G. Cole
May 23, 2023 (v1)
Keywords: Bayesian networks, condition monitoring, fault analysis, Machine Learning, probabilistic estimation, remaining useful life, survival analysis, vibration analysis
To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.
Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques
Xianlin Ma, Mengyao Hou, Jie Zhan, Zhenzhi Liu
May 23, 2023 (v1)
Keywords: interpretability, LIME, Machine Learning, SHAP, well productivity
Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their high complexity and low interpretability can hinder their practical application. This study proposes using interpretable ML solutions, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to provide explicit explanations of the ML prediction model. The study uses data from the Eastern Sulige tight gas field in the Ordos Basin, China, containing various geological and engineering factors. The results show that the gradient boosting decision tree model exhibits superior predictive performance compared to other ML models. The global interpretation using SHAP provides insights into the overall impact of these factors, while the local interpretation using SHAP and LIME offers individualized explanations o... [more]
Showing records 1 to 25 of 700. [First] Page: 1 2 3 4 5 Last
[Show All Keywords]