Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features

2020 ◽  
pp. 107754632092983
Author(s):  
Leonardo S Jablon ◽  
Sergio L Avila ◽  
Bruno Borba ◽  
Gustavo L Mourão ◽  
Fabrizio L Freitas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Machine Learning Algorithms ◽  
Vibration Measurement ◽  
Machine Learning Techniques ◽  
Rolling Element Bearings ◽  
Rotating Machines ◽  
Signal To Noise ◽  
Rotating Machine ◽  
Rolling Element

The diagnosis of failures in rotating machines has been subject to studies because of its benefits to maintenance improvement. Condition monitoring reduces maintenance costs, increases reliability and availability, and extends the useful life of critical rotating machinery in industry ambiance. Machine learning techniques have been evolving rapidly, and its applications are bringing better performance to many fields. This study presents a new strategy to improve the diagnosis performance of rotating machines using machine learning strategies on vibration orbital features. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit. On the other hand, rolling element bearings are prevalent in industrial machinery. This type of bearing has less orbital oscillation and is noisier than sliding contact bearings. Therefore, it is more difficult to extract useful information. Practical results on an industry motor workbench with rolling element bearings are presented, and the algorithm robustness is evaluated by calculating diagnosis accuracy using inputs with different signal-to-noise ratios. For this kind of noisy scenario where signal analysis is naturally tough, the algorithm classifies approximately 85% of the time correctly. In a completely harsh environment, where the signal-to-noise ratio can be smaller than −25 dB, the accuracy achieved is close to 60%. These statistics show that the strategy proposed can be robust for rotating machine unbalance condition diagnosis even in the worst scenarios, which is required for industrial applications.

scholarly journals A Review on Intelligent Fault Detection in Rolling Element Bearings

2020 ◽  
Vol 184 ◽  
pp. 01044
Author(s):  
Robin Raj Balraj ◽  
Madhavi Barla ◽  
Govardhan Tingarikar
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Vibration Signal ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Signal Estimation ◽  
Rolling Element Bearings ◽  
Bearing Diagnostics ◽  
Learning Techniques ◽  
Rolling Element

Rolling element bearings play vital role in the working of rotating hardware or machine. The imperfection-initiated vibration signal estimation and its examination is frequently utilized in deficiency recognition of direction. The crude sign is mind boggling in nature to dissect for deformity highlights, Therefore the sign be prepared to break down it. This article presents different sign handling procedures including canny strategies, for example, Artificial Techniques, Machine learning techniques and so on. The suitability of these strategies, all things considered, depends on the idea of features isolated from the bearing signs. The writing containing procedures utilized by different analysts have been managed in this review. This review may fill in as a kind of perspective for the scientists to go over different strategies in bearing diagnostics.

An Analysis of QSAR Research Based on Machine Learning Concepts

2020 ◽  
Vol 17 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Mehrnoush Barani Shirzad
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Biological Activities ◽  
Analytical Framework ◽  
Quantitative Structure Activity Relationship ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Qsar Modeling ◽  
Chemical Structures

: Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called ‘ML-QSAR‘. This framework has been designed for future research to: a)facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

scholarly journals Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2705 ◽  
Author(s):  
Xiaochuan Li ◽  
Faris Elasha ◽  
Suliman Shanbr ◽  
David Mba
Keyword(s):  
Machine Learning ◽  
Roller Bearing ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Rolling Element Bearings ◽  
Wind Turbine Gearbox ◽  
Rolling Element ◽  
Useful Life

Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.

scholarly journals Constructing a System for Analysis of Machine Learning Techniques for Early Detection of Thyroid

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1978-1981
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Classification Techniques ◽  
Safe Condition

Thyroid is an unending and complex infection caused by unedifying levels of TSH (Thyroid Simulation Hormone) or by thyroid organ problems themselves. Hashimoto's thyroid is the most widely recognized cause of hypothyroidism. The body makes anticorps that pulverize the thyroid organ in an auto-safe condition. It offers machine learning algorithms in the system proposed to predict thyroid disease in disease-intensive societies effectively. This is a serious concern for public health even though it is massively increasing in many countries. This shows that the problem must be predicted as urgently as possible to overcome the shortcomings of previously existing clinical decision-making tools with low precision. This paper examines numerous machine learning strategies for osteoporosis prediction. The paper examines and assesses the use of the strategy of feature selection combined with classification techniques. WEKA's classification techniques are used to measure an osteoporosis data set. The results are calculated by means of various test options, including 10-fold cross-validation, training sets and the percentage divided with and without the selection method. The results are compared with correctly classified instances, runtime, kappa and absolute mean values for experiments with and without feature selection techniques.

scholarly journals A Recent Machine Learning Techniques for Failure Diagnosis of Rolling Element Bearing

2021 ◽  
pp. 42-53
Author(s):  
Mohsin Hassan Albdery ◽  
István Szabó
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Essential Element ◽  
Rolling Element Bearing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Rotating Machines ◽  
Learning Techniques ◽  
Rolling Element ◽  
Critical Components

Rolling element bearings are critical components of rotating machines, and fault in the bearing can cause the machine to fail. Bearing failure is one of the leading causes of failure in various rotating machines used in industry at high and low speeds. Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is an essential element in the diagnosis of defects that saves time and expenses and avoids dangerous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article gives a short overview of recent trends in the use of machine learning for fault detection. Finally, Deep Learning techniques were recently developed to monitor the health of the intelligent machine are discussed.

scholarly journals EMERGING MACHINE LEARNING TECHNIQUES IN MALWARE DETECTION AND ANALYSIS: A COMPARATIVE ANALYSIS

2020 ◽  
Vol 8 (10) ◽  
pp. 771-779
Author(s):  
Prithvi Chintha ◽  
◽  
Kakelli Anil Kumar ◽  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Malware Analysis ◽  
Existing Problems ◽  
Learning Patterns ◽  
Current Trends ◽  
Learning Techniques

New types of malware with unique characteristics are being created daily in legion. This exponential increase in malwareis creating a threat to the internet. From the past decade, various techniques of malware analysis and malware detection have been developed to prevent the efficacy of malware. However, due to the fast-growing numbers and complexities in malware, it is getting difficult to detect and analyze the malware manually. Because of the inefficiency in manual malware analysis, automated malware detection and analysis would be a better solution. Thus, malware analysis supported by machine learning became a required part of malware analysis. The automation used in learning patterns in malware can help in efficiently identifying the complexities. Malware Analysis with help the Machine learning would be more efficacious in terms of automation and memory usage. In this paper, we conducted a review of emerging various ML (Machine Learning) strategies used so far, in the field of malware analysis, to give a comprehensive view of the existing processes. We systemized them on various aspects like their objectives, machine learning algorithms used, information about the malware, etc. We also highlighted the existing problems in this particular field of study and tried to find multiple ways in which advancements can happen concerning the current trends being used.

Using Machine Learning Algorithms on Prediction of Stock Price

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.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

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.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

Sign in / Sign up

Export Citation Format

Share Document