Author
Afrooz Purarjomandlangrudi
Other affiliations: Queensland University of Technology
Bio: Afrooz Purarjomandlangrudi is an academic researcher from Griffith University. The author has contributed to research in topics: Fault detection and isolation & Condition monitoring. The author has an hindex of 6, co-authored 13 publications receiving 174 citations. Previous affiliations of Afrooz Purarjomandlangrudi include Queensland University of Technology.
Papers
More filters
TL;DR: In this paper, a data mining approach using a machine learning technique called anomaly detection (AD) is presented, which employs classification techniques to discriminate between defect examples and two features, kurtosis and non-Gaussianity score (NGS), are extracted to develop anomaly detection algorithms.
104 citations
TL;DR: A clearer picture is proposed of studies have been conducted regarding online interaction and factors that impact it in online education systems to propose a clearer picture of success and persistence in such courses.
Abstract: Online learning has become a widespread method for providing learning at different levels of education. It has facilitated the learning in many ways and made it more flexible and available by providing learners with more opportunities to learn information, further access to different learning resources, and collaboration rather than face-to-face learning. In spite of these benefits and rapid growth of online education, success and persistence in such courses is one the important aspects of online learning research and it relies on different factors. Therefore investigating the reasons of students' dropout of an online education course or program and its contributing factors is essential in this area. One of the most barriers in online learning system is lack of interactions. In learning, interaction between students themselves, with the course content, and course instructors is important for conveying information, enhancing teaching quality, give directions, and many more functions. The aim of this research is to review the literature to propose a clearer picture of studies have been conducted regarding online interaction and factors that impact it in online education systems.
24 citations
TL;DR: The history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present), and various systems that applied AE technique in their health monitoring is discussed.
Abstract: Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.
22 citations
TL;DR: In this paper, the authors proposed a research model to assess the possible impact of the student's personal attributes and perceived course characteristics on their online interaction and engagement, which was collected by survey from 246 students who participated in online courses in one of Australia universities.
Abstract: One of the most pressing issues in online learning systems that have contributed to the failure of online education and student dropout is the lack of interaction Investigating and exploring the different factors that influence learners’ online interaction and engagement are crucial for e-learning success This study proposes a research model to assess the possible impact of the student’s personal attributes and perceived course characteristics on their online interaction and engagement The data of this study were collected by survey from 246 students who participated in online courses in one of Australia universities Partial least squares was then used as a method to test the research model and hypotheses
17 citations
TL;DR: In this paper, the authors employed a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012, and different techniques which have been used by researchers are introduced, classified and discussed.
Abstract: Wind power has become one of the popular renewable resources all over the world and is anticipated to occupy 12% of the total global electricity generation capacity by 2020. For the harsh environment that the wind turbine operates, fault diagnostic and condition monitoring are important for wind turbine safety and reliability. This paper employs a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012. The frequent faults and failures in wind turbines are considered and different techniques which have been used by researchers are introduced, classified and discussed.
17 citations
Cited by
More filters
TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.
871 citations
TL;DR: An overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed that integrated big data analytics and service-driven patterns that helped to overcome barriers in the implementation of CP.
351 citations
TL;DR: In this article, the authors discuss recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines.
Abstract: The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.
287 citations
TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.
257 citations
TL;DR: This paper aims at systematically and comprehensively summarizing current large-scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements, followed by a brief summary of future research directions for wind turbine Bearing fault diagnosis.
249 citations