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Rafia Nishat Toma

Rafia Nishat Toma
Khulna University (Bangladesh)

PhD Student - University of Ulsan - South Korea

About

19
Publications
6,933
Reads
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272
Citations
Introduction
Fault diagnosis with motor current signal analysis.
Additional affiliations
September 2019 - present
University of Ulsan
Position
  • Research Assistant
September 2013 - present
Khulna University
Position
  • Faculty Member
Education
September 2019 - August 2023
University of Ulsan
Field of study
  • Electrical, Electronics and Computer Engineering
September 2013 - July 2016
Khulna University
Field of study
  • Electronics and Communication Engineering
February 2008 - October 2012
Khulna University
Field of study
  • Electronics and Communication Engineering

Publications

Publications (19)
Article
Full-text available
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the b...
Chapter
Condition monitoring can avoid sudden breakdown and ensure the reliable and safe operation of rotating machinery used in the industry. The early detection of fault signatures and accurately classifying them in time will ensure efficient maintenance operation and reduce the possibility of losses due to uncertain breakdown. In the fault diagnosis mec...
Article
Full-text available
Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and moto...
Article
Full-text available
Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain...
Conference Paper
Bearing failure is considered as one of the major problems in induction motor, which can result a huge mechanical damage if is not monitored from the initial stage. A complete fault classification method is presented in this paper by combining wavelet-based signal processing technique and deep learning method for fault classification. Vibration sig...
Article
Full-text available
Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonline...
Chapter
Condition monitoring of induction motors plays a significant role in avoiding unexpected breakdowns and reducing excessive maintenance costs. In the majority of cases, bearing faults are found to be an issue in the failure of induction motors. The detection and valuation of irregularities at an early stage can help prevent disastrous failures. In t...
Article
Full-text available
Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motor...
Article
Full-text available
Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are...
Article
Full-text available
Among an electricity provider's non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantl...
Article
Full-text available
This paper deals with the design and study of parameters of square shaped microstrip patch antenna suitable for 5G communication systems. It is designed on Rogers RT Duroid 5880, which has a dielectric constant of 2.2. In this study, a micro-strip line fed patch antenna array, operating at a resonant frequency of 10.21GHz which is preferred for 5G...
Article
As the technology enters into deep sub-micron region, signal integrity is becoming a very crucial parameter. In order to deal with the challenges associated with signal integrity problem, such as, crosstalk noise and delay, estimation and minimizing techniques should be addressed with great importance. Along with this, the peak noise amplitude and...
Conference Paper
The ubiquitous computing era is characterized by wide deployment of resource constraint devices. Secure communication between such devices encounters a big challenge due to their resource limitation. The lightweight cryptographic algorithms are developed to address this challenge. In this work, implementation of LBlock, a lightweight encryption alg...
Conference Paper
In this paper, the usefulness of parabolic index profile with single clad as well as double clad fiber structure has been investigated for the design of erbium doped fiber amplifier (EDFA) and laser (EDFL). The characteristics of parabolic profile fiber lasers in terms of couple pump power and cavity length have been simulated for 980 nm pump and 1...
Conference Paper
Full-text available
The modeling of interconnections is very much important, as the performance of VLSI circuit is limited by interconnect related failure modes, such as coupled noise and delay. Inductance along with capacitance causes noise in the signals, which may adversely affect the performance of the circuit and signal integrity. An analytical expression for cro...

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Projects

Projects (2)
Project
As tittle under 'PhD Research', the objective of this project is to extend cooperation and collaboration among similar-interest researchers through Q&A. Currently, project comprises on following research interests, 1. Data-driven fault diagnosis and prognosis 2. Model based fault detection and diagnosis 3. AI application on machinery condition monitoring 4. Application of signal processing