Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.

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A Machine Learning Based Approach of Performance Estimation for High-Pressure Compressor Airfoils

Volume 2D: Turbomachinery ◽

10.1115/gt2018-75372 ◽

2018 ◽

Author(s):

Jonas Marx ◽

Stefan Gantner ◽

Jörn Städing ◽

Jens Friedrichs

Keyword(s):

Machine Learning ◽

High Pressure ◽

Machine Learning Algorithms ◽

Performance Estimation ◽

Predictive Maintenance ◽

Support Vector ◽

K Nearest Neighbor ◽

Operating Characteristics ◽

High Pressure Compressor ◽

Pressure Compressor

In recent years, the demands of Maintenance, Repair and Overhaul (MRO) customers to provide resource-efficient after market services have grown increasingly. One way to meet these requirements is by making use of predictive maintenance methods. These are ideas that involve the derivation of workscoping guidance by assessing and processing previously unused or undocumented service data. In this context a novel approach on predictive maintenance is presented in form of a performance-based classification method for high pressure compressor (HPC) airfoils. The procedure features machine learning algorithms that establish a relation between the airfoil geometry and the associated aerodynamic behavior and is hereby able to divide individual operating characteristics into a finite number of distinct aero-classes. By this means the introduced method not only provides a fast and simple way to assess piece part performance through geometrical data, but also facilitates the consideration of stage matching (axial as well as circumferential) in a simplified manner. It thus serves as prerequisite for an improved customary HPC performance workscope as well as for an automated optimization process for compressor buildup with used or repaired material that would be applicable in an MRO environment. The methods of machine learning that are used in the present work enable the formation of distinct groups of similar aero-performance by unsupervised (step 1) and supervised learning (step 2). The application of the overall classification procedure is shown exemplary on an artificially generated dataset based on real characteristics of a front and a rear rotor of a 10-stage axial compressor that contains both geometry as well as aerodynamic information. In step 1 of the investigation only the aerodynamic quantities in terms of multivariate functional data are used in order to benchmark different clustering algorithms and generate a foundation for a geometry-based aero-classification. Corresponding classifiers are created in step 2 by means of both, the k Nearest Neighbor and the linear Support Vector Machine algorithms. The methods’ fidelities are brought to the test with the attempt to recover the aero-based similarity classes solely by using normalized and reduced geometry data. This results in high classification probabilities of up to 96 % which is proven by using stratified k-fold cross-validation.

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Machine learning prediction of interaction energies in rigid water clusters

Physical Chemistry Chemical Physics ◽

10.1039/c8cp03138j ◽

2018 ◽

Vol 20 (35) ◽

pp. 22987-22996 ◽

Cited By ~ 2

Author(s):

Samik Bose ◽

Diksha Dhawan ◽

Sutanu Nandi ◽

Ram Rup Sarkar ◽

Debashree Ghosh

Keyword(s):

Machine Learning ◽

Support Vector Regression ◽

Water Clusters ◽

High Accuracy ◽

Support Vector ◽

Interaction Energies ◽

Many Body ◽

New Machine

A new machine learning based approach combining support vector regression (SVR) and many body expansion (MBE) that can predict the interaction energies of water clusters with high accuracy (for decamers: 2.78% of QM estimates).

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Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

Indonesian Journal of electronics, electromedical engineering, and medical informatics ◽

10.35882/ijeeemi.v3i1.2 ◽

2021 ◽

Vol 3 (1) ◽

pp. 9-14

Author(s):

Tsehay Admassu Assegie

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Liver Disease ◽

Classification Model ◽

Support Vector ◽

Disease Prediction ◽

Accuracy Score ◽

Learning Models ◽

Accuracy And Precision ◽

Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.

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FINDING TEMPORAL STRUCTURE IN TEXT: MACHINE LEARNING OF SYNTACTIC TEMPORAL RELATIONS

International Journal of Semantic Computing ◽

10.1142/s1793351x07000238 ◽

2007 ◽

Vol 01 (04) ◽

pp. 441-457 ◽

Cited By ~ 5

Author(s):

STEVEN BETHARD ◽

JAMES H. MARTIN ◽

SARA KLINGENSTEIN

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Temporal Structure ◽

Learning Model ◽

High Accuracy ◽

Support Vector ◽

Automatic Extraction ◽

Temporal Relations ◽

Tense And Aspect ◽

Machine Learning Model

This research proposes and evaluates a linguistically motivated approach to extracting temporal structure from text. Pairs of events in a verb-clause construction were considered, where the first event is a verb and the second event is the head of a clausal argument to that verb. All pairs of events in the TimeBank that participated in verb-clause constructions were selected and annotated with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, support vector machine (SVM) models were trained which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of temporal structure from text.

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A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Sustainability ◽

10.3390/su12093612 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3612 ◽

Cited By ~ 3

Author(s):

Davut Solyali

Keyword(s):

Machine Learning ◽

Power Generation ◽

Historical Data ◽

Sustainable Growth ◽

Solar Irradiation ◽

Support Vector ◽

Short Term ◽

Inference System ◽

Electricity Load

Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

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Machine Learning Approach to Predictive Maintenance in Manufacturing Industry - A Comparative Study

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2020.4.006 ◽

2021 ◽

Vol 2 (4) ◽

pp. 246-255

Author(s):

Karrupusamy P

Keyword(s):

Machine Learning ◽

Communication Networks ◽

Asset Management ◽

Information Acquisition ◽

Manufacturing Industry ◽

Predictive Maintenance ◽

Utilization Rate ◽

Accuracy And Precision ◽

Machine Learning Approach ◽

Maintenance Process

Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.

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Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features

Journal of Artificial Intelligence and Soft Computing Research ◽

10.1515/jaiscr-2015-0033 ◽

2015 ◽

Vol 5 (4) ◽

pp. 247-255 ◽

Cited By ~ 10

Author(s):

Masaki Murata ◽

Satoshi Ito ◽

Masato Tokuhisa ◽

Qing Ma

Keyword(s):

Machine Learning ◽

High Accuracy ◽

Supervised Machine Learning ◽

Feature Analysis ◽

Support Vector ◽

Baseline Method ◽

Sentence Generation ◽

New Words ◽

Order Estimation ◽

Textual Features

Abstract In this paper, we propose a method to estimate the order of paragraphs by supervised machine learning. We use a support vector machine (SVM) for supervised machine learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.84) in the order estimation experiments of the first two paragraphs of an article. In addition, it obtained a higher accuracy than the baseline method in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs.

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Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports

Frontiers in Digital Health ◽

10.3389/fdgth.2021.692077 ◽

2021 ◽

Vol 3 ◽

Author(s):

Antoine Pironet ◽

Hélène A. Poirel ◽

Tim Tambuyzer ◽

Harlinde De Schutter ◽

Lien van Walle ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cancer Registry ◽

Support Vector ◽

Free Text ◽

Breast Cancer Patients ◽

Receptor Status ◽

Tyrosine Kinase 2 ◽

The Status ◽

Pathology Reports

As part of its core business of gathering population-based information on new cancer diagnoses, the Belgian Cancer Registry receives free-text pathology reports, describing results of (pre-)malignant specimens. These reports are provided by 82 laboratories and written in 2 national languages, Dutch or French. For breast cancer, the reports characterize the status of estrogen receptor, progesterone receptor, and Erb-b2 receptor tyrosine kinase 2. These biomarkers are related with tumor growth and prognosis and are essential to define therapeutic management. The availability of population-scale information about their status in breast cancer patients can therefore be considered crucial to enrich real-world scientific studies and to guide public health policies regarding personalized medicine. The main objective of this study is to expand the data available at the Belgian Cancer Registry by automatically extracting the status of these biomarkers from the pathology reports. Various types of numeric features are computed from over 1,300 manually annotated reports linked to breast tumors diagnosed in 2014. A range of popular machine learning classifiers, such as support vector machines, random forests and logistic regressions, are trained on this data and compared using their F1 scores on a separate validation set. On a held-out test set, the best performing classifiers achieve F1 scores ranging from 0.89 to 0.92 for the four classification tasks. The extraction is thus reliable and allows to significantly increase the availability of this valuable information on breast cancer receptor status at a population level.

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Alzheimer Disease Classification using Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2942.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 6-9

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Developed Countries ◽

Disease Classification ◽

Support Vector ◽

Data Set ◽

Classification Technique ◽

Routine Tasks ◽

Memory Power ◽

To Come

Alzheimer's disease is the most popular and persuading dementia that affects our memory power, reasoning and deportment. Symptoms rise up slowly and worsen with time, becoming an obstacle in doing our routine tasks. Alzheimer is not conventional wedge of aging. The substantial and known risk factor is up surging age. The prevalence of AD is depicted to be around 5% after an age of 65 years and took a leap of 30% for people of 85 years old in developed countries [1]. In this project we proposed a detection and classification technique using Random Forest(RF) and Support Vector Machine(SVM) algorithms on the oasis longitudinal data set and compare their respective accuracies to come to a conclusion that which algorithm best suits for this detection and classification. paper Setup must be in A4 size with Margin: Top 0.7”,

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Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.0813944 ◽

2021 ◽

Vol 8 (1) ◽

pp. 147

Author(s):

Primandani Arsi ◽

Retno Waluyo

Keyword(s):

Machine Learning ◽

Social Media ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Public Discourse ◽

Capital City ◽

Support Vector ◽

The Public ◽

Machine Learning Methods ◽

Classification Technique

Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari pihak yang merasa pro dan kontra, masing-masing memiiki sudut pandang yang berbeda. Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode machine learning terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode Support Vector Machine (SVM) untuk diterapkan pada tweets topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial twitter. Teknis klasifikasi dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap tweets sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 tweets (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, precision=95.82%, recall=94.04% dan AUC = 0,979. AbstractToday, social media is growing fast on the internet.One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.

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