scholarly journals Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 453
Author(s):  
Hisahide Nakamura ◽  
Yukio Mizuno
Keyword(s):  
Machine Learning ◽  
Induction Motors ◽  
Diagnostic Methods ◽  
Industrial Processes ◽  
Rotating Speed ◽  
Repair Costs ◽  
Reducing Costs ◽  
New Feature ◽  
The Relationship ◽  
Financial Losses

Induction motors are widely used in industry and are essential to industrial processes. The faults in motors lead to high repair costs and cause financial losses resulting from unexpected downtime. Early detection of faults in induction motors has become necessary and critical in reducing costs. Most motor faults are caused by bearing failure. Machine learning-based diagnostic methods are proposed in this study. These methods use effective features. First, load currents of healthy and faulty motors are measured while the rotating speed is changing continuously. Second, experiments revealed the relationship between the magnitude of the amplitude of specific signals and the rotating speed, and the rotating speed is treated as a new feature. Third, machine learning-based diagnoses are conducted. Finally, the effectiveness of machine learning-based diagnostic methods is verified using experimental data.

Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques

2014 ◽  
pp. 193-202
Author(s):  
LinRuchika Malhotra ◽  
Ankita Jain Bansal
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Maintenance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Change Proneness ◽  
Regression Techniques ◽  
Reducing Costs ◽  
Boosting Technique ◽  
The Relationship

For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques

2017 ◽  
pp. 2043-2052
Author(s):  
LinRuchika Malhotra ◽  
Ankita Jain Bansal
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Maintenance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Change Proneness ◽  
Regression Techniques ◽  
Reducing Costs ◽  
Boosting Technique ◽  
The Relationship

For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

DIAGNOSING QUALITY OF FRICTION SYSTEMS OF MECHANISMS OF DEVICES

2019 ◽  
Vol 1 (7) ◽  
pp. 10-13
Author(s):  
D. Yu. Ershov ◽  
I. N. Lukyanenko ◽  
E. E. Aman
Keyword(s):  
Surface Defects ◽  
Diagnostic Methods ◽  
Design Parameters ◽  
Diagnostic Model ◽  
Mechanical Assemblies ◽  
Local Defects ◽  
Relationship Of ◽  
The Relationship ◽  
Production And Operation

The article shows the need to develop diagnostic methods for monitoring the quality of lubrication systems, which makes it possible to study the dynamic processes of contacting elements of the friction systems of instrument mechanisms, taking into account roughness parameters, the presence of local surface defects of elements and the bearing capacity of a lubricant. In the present article, a modern diagnostic model has been developed to control the quality of the processes of production and operation of friction systems of instrument assemblies. With the help of the developed model, it becomes possible to establish the relationship of diagnostic and design parameters of the mechanical system, as well as the appearance of possible local defects and lubricant state, which characterize the quality of friction systems used in many mechanical assemblies of the mechanisms of devices. The research results are shown in the form of nomograms to assess the defects of the elements of friction mechanisms of the mechanisms of the devices.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 105
Author(s):  
Khaleel Husain ◽  
Mohd Soperi Mohd Zahid ◽  
Shahab Ul Hassan ◽  
Sumayyah Hasbullah ◽  
Satria Mandala
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Privacy Preservation ◽  
Causes Of Death ◽  
Clinical Settings ◽  
Data Format ◽  
Medical Institutions ◽  
Comprehensive Survey ◽  
Electrocardiogram Ecg ◽  
The Relationship

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

scholarly journals Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

2021 ◽  
Vol 13 (11) ◽  
pp. 6376
Author(s):  
Junseo Bae ◽  
Sang-Guk Yum ◽  
Ji-Myong Kim
Keyword(s):  
Machine Learning ◽  
Financial Management ◽  
Construction Projects ◽  
Transportation Infrastructure ◽  
Classification Model ◽  
Insurance Companies ◽  
Economic Damage ◽  
Infrastructure Projects ◽  
Machine Learning Classification ◽  
Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

scholarly journals The relationship between trust in AI and trustworthy machine learning technologies

2020 ◽  
Author(s):  
Ehsan Toreini ◽  
Mhairi Aitken ◽  
Kovila Coopamootoo ◽  
Karen Elliott ◽  
Carlos Gonzalez Zelaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
The Relationship
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