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Structural Durability & Health Monitoring 2017年第2期11卷 191-208页

作者： P.S.Anoop V.Sugumaran School of Mechanical and Building Sciences(smbs) Vellore Institute of Technology Chennai campusChennaiIndia

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European *** existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm *** this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an *** obtained signals will be used to compute through statistical features and histogram features for the feature extraction *** LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram *** proposed model can be used for monitoring the automobile tyre pressure successfully.

关键词： Machine learning Vibration accelerometer Statistical Features Histogram Features Logistic model tree(LMT) Tyre pressure monitoring system

A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals

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Structural Durability & Health Monitoring 2017年第1期11卷 63-79页

作者： A.Joshuva V.Sugumaran Research Associate School of Mechanical and Building Sciences(SMBS)VIT UniversityChennai CampusVandalur-Kelambakkam RoadChennai 600127India Associate Professor School of Mechanical and Building Sciences(SMBS)VIT UniversityChennai CampusVandalur-Kelambakkam RoadChennai 600127India

Renewable energy sources are considered much in energy fields because of thecontemporary energy calamities. Among the important alternatives being considered, windenergy is a durable competitor because of its dependability due to the development of theinnovations, comparative cost effectiveness and great framework. To yield wind energymore proficiently, the structure of wind turbines has turned out to be substantially bigger,creating conservation and renovation works troublesome. Due to various ecologicalconditions, wind turbine blades are subjected to vibration and it leads to failure. If thefailure is not diagnosed early, it will lead to catastrophic damage to the framework. In orderto increase safety observations, to reduce down time, to bring down the recurrence ofunexpected breakdowns and related enormous maintenance, logistic expenditures and tocontribute steady power generation, the wind turbine blade must be monitored now andthen to assure that they are in good condition. In this paper, a three bladed wind turbinewas preferred and using vibration source, the condition of a wind turbine blade is *** faults like blade crack, erosion, hub-blade loose connection, pitch angle twist and bladebend faults were considered and these faults are classified using Bayes Net (BN),Discriminative Multinomial Naïve Bayes (DMNB), Naïve Bayes (NB), Simple NaïveBayes (SNB), and Updateable Naïve Bayes (UNB) classifiers. These classifiers arecompared and better classifier is suggested for condition monitoring of wind turbine blades.

关键词： Condition monitoring fault diagnosis wind turbine blade machine learning statistical features vibration signals

Pulsed current and dual pulse gas metal arc welding of grade AISI: 310S austenitic stainless steel

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Defence Technology（防务技术） 2015年第3期11卷 269-274页

作者： A.MATHIVANAN A.SENTHILKUMAR K.DEVAKUMARAN Mechanical Engineering Department SRM University School of Mechanical and Building Sciences (smbs) VIT University Welding Research Institute Bharath Heavy Electricals Limited (BHEL)

The transverse shrinkage, mechanical and metallurgical properties of AISI: 310 S ASS weld joints prepared by P-GMAW and DP-GMAW processes were investigated. It was observed that the use of the DP-GMAW process improves the aforementioned characteristics in comparison to that of the P-GMAW process. The enhanced quality of weld joints obtained with DP-GMAW process is primarily due to the combined effect of pulsed current and thermal pulsation(low frequency pulse). During the thermal pulsation period, there is a fluctuation of wire feed rate,which results in the further increase in welding current and the decrease in arc voltage. Because of this synchronization between welding current and arc voltage during the period of low frequency pulse, the DP-GMAW deposit introduces comparatively more thermal shock compared to the P-GMAW deposit, thereby reducing the heat input and improves the properties of weld joints.

关键词： AISI 电弧焊接奥氏体不锈钢脉冲气体电流脉冲品位金属力学性能

Frequencies of circular plate with concentric ring and elastic edge support

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Frontiers of Mechanical Engineering 2014年第2期9卷 168-176页

作者： Lokavarapu Bhaskara RAO Chellapilla Kameswara RAO smbs VIT University Chennai Campus Vandalur-Kelambakkam Road Chennai-600127 Tamil Nadu India Department of Mechanical Engineering Guru Nanak Institutions Technical Campus lbrahimpatnam Hyderabad - 501506 A.P India

Exact solutions for the flexural vibrations of circular plates having elastic edge conditions along with rigid concentric ring support have been presented in this paper. Values of frequency parameter for the considered circular plate are computed for different sets of values of elastic rotational and translation restraints and the radius of internal rigid ring support. The results for the first three modes of plate vibrations are computed and are presented in tabular form. The effects of rotational and linear restraints and the radius of the rigid ring support on the vibration behavior of circular plates are studied over a wide range of non-dimensional parametric values. The values of the exact frequency parameter presented in this paper for varying values of restraint parameters and the radius of the rigid ring support can better serve in design and as benchmark solutions to validate the numerical methods obtained by using other methods of solution.

关键词： circular plate frequency elastic edge rigid ring mode switching

Comparative Study on Tree Classifiers for Application to Condition Monitoring ofWind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

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Structural Durability & Health Monitoring 2019年第4期13卷 399-416页

作者： A.Joshuva V.Sugumaran Centre for Automation and Robotics(ANRO) Department of Mechanical EngineeringHindustan Institute of Technology and SciencePadurChennai 603103India School of Mechanical and Building Sciences(smbs) VIT UniversityChennai CampusVandalur-Kelambakkam RoadChennai 600127India

Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other *** wind turbine is an essential system used to change kinetic energy into electrical *** turbine blades,in particular,require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine *** main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach through histogram *** this study,blade bend,hub-blade loose connection,blade erosion,pitch angle twist,and blade cracks were simulated on the *** problem is formulated as a machine learning problem which consists of three phases,namely feature extraction,feature selection and feature *** features are extracted from vibration signals and feature selection was carried out using the J48 decision tree *** classification was performed using 15 tree *** results of the machine learning classifiers were compared with respect to their accuracy percentage and a better model is suggested for real-time monitoring of a wind turbine blade.

关键词： Condition monitoring fault diagnosis wind turbine blade machine learning histogram features tree classifiers

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

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Structural Durability & Health Monitoring 2019年第2期13卷 181-203页

作者： A.Joshuva V.Sugumaran Centre for Automation and Robotics(ANRO) Department of Mechanical EngineeringHindustan Institute of Technology and SciencePadurChennai603103India School of Mechanical and Building Sciences(smbs) VIT UniversityChennai CampusVandalur-Kelambakkam RoadChennai600127India

Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable aerodynamic forces,lightning strikes and gravitational loads which causes crack on the surface of wind turbine *** is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind *** this paper,a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine *** models are built based on computing the vibration response of the blade when it is excited using piezoelectric *** statistical,histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.

关键词： Structural health monitoring fault diagnosis blade crack statistical features histogram features ARMA features tree algorithms vibration signals

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

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Computers, Materials & Continua 2017年第2期53卷 157-174页

作者： Susom Dutta ARamachandra Murthy Dookie Kim Pijush Samui Undergraduate Student School of Mechanical&Building Sciences(SMBS)VIT UniversityVelloreTamil Nadu 632014India.Email:susomdutta7@*** Senior Scientist Computational Structural Mechanics GroupCSIR-Structural Engineering Research CentreTaramaniChennai-600113.Email:murthyarc@serc.res.in Professor Department of Civil EngineeringKunsan National UniversityKunsanJeonbukSouth Korea.Email:kim2kie@*** Associate Professor National Institute of TechnologyPatnaIndiaEmail:pijush@nitp.ac.in.

In the present scenario,computational modeling has gained much importance for the prediction of the properties of *** paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting *** contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is *** experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

关键词： Self Compacting Concrete(SCC) Compressive Strength Extreme Learning Machine(ELM) Adaptive Neuro Fuzzy Inference System(ANFIS) Multi Adaptive Regression Spline(MARS).