Designing predictive maintenance systems using decision tree-based machine learning techniques

PurposeThe after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.Design/methodology/approachWe propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.FindingsWe found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.Research limitations/implicationsThis approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.Practical implicationsThis approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.Social implicationsSustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.Originality/valueThis is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

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Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1045.0785s319 ◽

2019 ◽

Vol 8 (5S3) ◽

pp. 192-195

Keyword(s):

Machine Learning ◽

Open Source ◽

Open Source Software ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Software Applications ◽

Learning Techniques ◽

Software Maintainability ◽

Quality Aspect

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

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Machine Learning Based Indoor Localisation Using Wi-Fi And Smartphone

Journal of Independent Studies and Research - Computing ◽

10.31645/06 ◽

2020 ◽

Author(s):

Zulqarnain Khokhar ◽

◽

Murtaza Ahmed Siddiqi ◽

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Indoor Localization ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Smart Devices ◽

Gradient Boosting ◽

Learning Techniques ◽

Indoor Localisation

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

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Performance evaluation of fourteen machine learning algorithms on credit card default classification

Journal of Education College Wasit University ◽

10.31185/eduj.vol1.iss26.103 ◽

2018 ◽

Vol 1 (26) ◽

pp. 461-474

Author(s):

Hussein Altabrawee

Keyword(s):

Machine Learning ◽

Decision Making ◽

Credit Card ◽

Main Idea ◽

Machine Learning Algorithms ◽

Financial Data ◽

Machine Learning Techniques ◽

Classification Models ◽

Learning Techniques ◽

Credit Card Default

Banks process their financial data by machine learning techniques to get knowledge from the data and use that knowledge in decision making and risk management. In this research, fourteen classification models have been built and trained using a real financial data from a bank in Taiwan. The models forecast the credit card default of a customer which is the repayment delay of the credit granted to the customer. The main idea of the research is evaluating and comparing the models based on their predictive average class accuracy

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Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

International Journal of Emerging Markets ◽

10.1108/ijoem-05-2020-0577 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Önder Özgür ◽

Uğur Akkoç

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Elastic Net ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Content Type ◽

Shrinkage Methods ◽

Inflation Forecasting ◽

Turkish Economy ◽

Learning Techniques

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

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"Predicting Absenteeism at Work Using Machine Learning Algorithms

Muthanna Journal of Pure Science ◽

10.52113/2/07.01.2020/1-12 ◽

2019 ◽

Vol 7 (1) ◽

pp. 1-12

Author(s):

Samir Qaisar Ajmi

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Tree Model ◽

Learning Techniques ◽

Business Market ◽

Commercial Environment

"To work in the commercial environment, the company needs to be a major competitor in the business market, which depends mainly on the company's resources. One of the most important resources is the employees. Based on that, the absence of the employees from work leads to deterioration and reduce production in the institutions which leads to heavy losses. There are many reasons why employees are absent from work. Those may include health problems and social occasions. The purpose of this paper was to apply machine learning techniques to predict the absenteeism at work. There are four methods have been used in this research ( neural network(NN) technique ,decision tree (DT) technique, support vector machine (SVM) technique and logistic regression (LR) technique. . decision tree model has the highest accuracy equals to 83.33% with AUC 0.834 and the support vector machine has the lowest accuracy equals to 68.47 % with AUC 0.760."

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K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

Atmosphere ◽

10.3390/atmos12070834 ◽

2021 ◽

Vol 12 (7) ◽

pp. 834

Author(s):

Dan Lou ◽

Mengxi Yang ◽

Dawei Shi ◽

Guojie Wang ◽

Waheed Ullah ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Large Scale ◽

Poyang Lake ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Lake Basin ◽

Area Index ◽

Poyang Lake Basin ◽

Learning Techniques

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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