A5010671404- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Structural Health Monitoring Techniques
- Fault Detection and Control Systems
- Engineering Diagnostics and Reliability
- Railway Engineering and Dynamics
- Infrastructure Maintenance and Monitoring
- Structural Engineering and Vibration Analysis
- Advanced machining processes and optimization
- Ultrasonics and Acoustic Wave Propagation
- Fractional Differential Equations Solutions
- Induction Heating and Inverter Technology
- Spectroscopy and Chemometric Analyses
- Market Dynamics and Volatility
- Concrete Corrosion and Durability
- Advanced DC-DC Converters
- Advanced Control Systems Design
- Transport Systems and Technology
- Photovoltaic System Optimization Techniques
- Financial Risk and Volatility Modeling
- ECG Monitoring and Analysis
- Metaheuristic Optimization Algorithms Research
- Energy Load and Power Forecasting
Princeton University
2018
Madan Mohan Malaviya University of Technology
2015-2017
Deen Dayal Upadhyaya Gorakhpur University
2016
National Institute of Technology Hamirpur
2012-2013
Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) system for rotating machinery. This paper experimentally explores development of a random forest (RF) classifier, recently emerged machine learning technique, multi-class mechanical fault bearing an induction motor. Firstly, vibration signals are collected from using accelerometer sensor. Parameters signal extracted form statistical features used as input feature classification problem. These...
Condition monitoring and fault diagnosis of equipment processes are great concern in industries. Early detection machineries can save money emergency maintenance cost. Therefore, it is necessary to various parts the machine. In this paper we present running speed frequency bearing defect frequencies an induction motor using vibration data through wavelet transform Hilbert transform. Bearing at which roller elements pass over a point. The analysis result shows that proposed method diagnose...
AbstractIn the present paper, hybrid bearing faults classification scheme based on wavelet transformation and neural network is proposed. Basically, proposed methodology identifies four different types of faults. For faults, vibration signals have been utilized. The are first decomposed into components in sub-bands using discrete transformation. Subsequently, variance autocorrelation value extracted from signal used as input features for network. time interval between impacts original also...
Abstract The rolling element bearings are used broadly in many machinery applications. It is to support the load and preserve clearance between stationary rotating elements. Unfortunately, exceedingly prone premature failures. Vibration signal analysis has been widely faults detection of can be classified as being a or non-stationary signal. In case faulty bearing vibration not strictly phase locked rotational speed shaft become “transient” nature. purpose this paper briefly discuss...
The ECG is an important clinical tool to diagnose or monitor various cardiac diseases. Like other electrical signals, the signal also corrupted by kinds of noise artifacts which affect diagnosis interpretation and leads erroneous results. In order a abnormality, accurate noiseless required. this paper, denoising algorithm based on Empirical Mode Decomposition (EMD) has been proposed. performance present compared with established methods. comparison performed basis statistical tools...
Purpose This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model which hammer blows are applied for excitation. The vibration response signals of structure collected using sensors placed at different nodes. damaged states such as no damage, single double damage and triple introduced by cutting members bridge. masked noise with recorded responses generates challenge properly analyze health structure. Design/methodology/approach analytical modal...
Purpose This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in damaged and retrofitted state through vibration response signals. Design/methodology/approach The analysis signals is performed time time-frequency domains using statistical features-root mean square, impulse factor, crest kurtosis, peak2peak Stockwell transform. proposed methodology uses Hilbert transform combination with spectral kurtosis bandpass filtering technique for obtaining...
Faults in induction machines are preventable and treatable upon early detection. Among various machine faults, failure of the bearing is considered as leading cause shutdown industries accounted approximately 41%. It characterized by persisting vibration signal which usually very low amplitude associated with frequencies. An investigation has been carried out on enhancing diagnostic relevance measurement using identification representative features classification damage, location damage...
In the present article work is carried out on scaled modeled bridge for condition assessment due to seeded damage. The objective find location of damage in steel using vibration signal. For differentiation between and intact condition, time, frequency domain analysis has been used. Power spectral density applied signal extract mode shapes compare healthy state modeled. Further, Short Time Fourier Transform gives 3D visualization amplification different which helps identify location. Using...
The purpose of this paper is to bring light the importance time series simulation methods which accurately replicate crossing times stochastic processes. A a contiguous block for process above or below some benchmark such as forecast. In addition bringing attention issue, we present family models, call state models (both univariate and multivariate are introduced), that outperform standard modeling techniques in their ability these times. This verified using weighted quadratic empirical...
In induction machine, faults can be avoided if it is detected early. Bearing failure the major cause and accounts for up to 41% of that occur in rotating machines. This fault distinguished by vibration signals whose amplitude however low linked with frequencies. A study accomplished magnify diagnostic pertinence measurement using recognition indicative features damage classification, location, severity. To accomplish task, a composite feature pool developed calculating from different...
With the development of renewable energy resources power grid was modernized with improved efficiency, clean power, replenishing and sustainability generation power. As we all know that solar is green sources they do not cause any harm to environment. Therefore, increasing, day by demand can be fulfilled because pollute environment does nature. In conventional system, MPPT responsible for extracting maximum possible from photovoltaic feed it load via boost converter which steps up voltage...