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8th International Conference on Acoustics and Vibration

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  KeyNote Speakers
Professor Mehdi Behzad,
Condition Monitoring Center, Sharif University of Technology
http://sharif.ir/~m_behzad/

Dr. Mehdi Behzad is founding director of the condition monitoring center at Sharif University of Technology, where he has conducted annual condition monitoring and fault diagnosis (CMFD) conference since 2006. He has a strong background in applying Condition Monitoring in a variety of Industries. This includes vibration analysis and problem shooting in power plants, oil, gas, petrochemical, still, cement and other industries. Along with his teaching engagement at Sharif University, Dr. Behzad runs vibration analysis courses for industries too. Dr. Behzad is a prolific researcher in vibration condition monitoring and remaining useful life calculation. He has published numerous papers in journals and conferences in this area apart from his two books on applied vibration and condition monitoring. Professor Mehdi Behzad is currently a member of WG6 in ICNDT for preparing Guide to qualification and Certification of personnel for Condition Monitoring.


Machinery Vibration Monitoring

Machine vibration that is not detected early enough will often lead to severe machine damage requiring costly repairs or even total machine replacement. Standards and guidelines on machine vibration monitoring will be surveyed in this paper. Different application of displacement, velocity and acceleration in machinery vibration survey will be discussed with industrial case studies. Low frequency and high frequency analysis of the vibration lead to different results when one uses the acceleration signal for fault diagnosis. Case studies will be addressed to illustrate these differences. In the second part, remaining useful life (RUL) calculation based on vibration acceleration trend in rolling element bearings (REBs) will be discussed. Almost fifty percent of failures in rotating machineries are because of REBs damage. Therefore, the more accurate RUL prediction of REBs leads to considerable enhancement of reliability in these kind of equipment. According to ISO 13381-1:2015, five general approach for prognostics modeling are: physics-based models, statistical models, heuristic models, data-driven models, and hybrid models. Artificial intelligence (AI) approaches are the most common methods in data-driven group. In this lecture, the application of AI approaches for prognostics will be discussed. Main challenges and capabilities of these methods will be reviewed. Beside the AI algorithm, selecting an appropriate feature for inputs of AI model has a crucial role in performance of prognostics model. A group of features extracted from high-frequency bandwidth of vibration spectrum are introduced. Then, two algorithms based on feedforward neural network (FFNN) are developed for online and offline condition monitoring data. Using these algorithms, the results of RUL prediction on bearing accelerated life tests data as well as industrial machine vibration monitoring data will be presented and discussed.

Dr Maryam Ghandchi Tehrani,
Institute of Sound and Vibration Research (ISVR)-University of Southampton
http://www.southampton.ac.uk/engineering/about/staff/mgt1y09.page

Dr Maryam Ghandchi Tehrani is Associate Professor in Active Control within Engineering and the Environment at the University of Southampton. She has a BSc in Mechanical Engineering from the Iran University of Technology (2002), an MSc (Eng) from the University of Liverpool (2004) and a PhD also from Liverpool (2007). She did two year postdoctoral research and one year temporary lectureship also at the University of Liverpool. She has been appointed as a new lecturer in the Institute of Sound and Vibration Research (ISVR) at the University of Southampton from October 2010. Her research areas are mainly vibration and control. In 2007 she began work as a post-doctoral researcher for two years on EPSRC funded research on “A New Approach for Active Vibration Suppression” with Professor Mottershead. This led to new developments not only for the assignment of poles and zeros, but also for the assignment of eigenvalue sensitivities. She won an IMechE prize for her presentation on Active Vibration Control. In the summer of 2010, she joined the ISVR Signal Processing and Control Group at the University of Southampton as a Lecturer.


Nonlinear Strategies in Vibration Energy Harvesters

In this paper, nonlinear strategies to improve the performance of vibration energy harvesters is presented. Firstly, an overview of energy harvesters with their applications is provided. One particular application is in civil engineering for example, to generate electrical power from vibrations to sensors that are distributed on bridges for structural health monitoring. Nonlinear damping such as cubic damping and level-dependent damping are introduced to the problem of energy harvesting to improve their performance. It has been demonstrated that the introduction of nonlinear damping can increase the dynamic performance range of energy harvesters. Two experimental vibration energy harvesters are designed and nonlinear strategies are implemented to improve their power harvested. The first harvester is a spring-mass-damper system with a ball-screw mechanism and the second harvester is a pendulum-type energy harvester. It has been shown that nonlinear damper can increase the amount of harvested power compared to the linear damper when operating at resonance. In addition, the use of level-dependent damper can maintain the device at its optimum operating condition, enabling to harvest the maximum possible power. Then, the use of impulsive damping has been considered. The impulsive damping can also be beneficial in reducing the dissipated energy in the system and therefore harvesting more energy from the system. The effect of the number of impulses and the intensity on the harvested power is also discussed. Finally, a practical example of energy harvesting from vibrations of the passage of an Intercity125 UK train is also provided and the power harvested is calculated from the measured time-acceleration on the track.

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