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Fault Detection and Diagnostics in an Ocean Turbine Using Vibration Analysis

[+] Author Affiliations
Mustapha Mjit, Pierre-Philippe J. Beaujean, David J. Vendittis

Florida Atlantic University, Dania Beach, FL

Paper No. IMECE2010-40963, pp. 535-543; 9 pages
doi:10.1115/IMECE2010-40963
From:
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 8: Dynamic Systems and Control, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4445-8
  • Copyright © 2010 by ASME

abstract

This paper describes the approach, procedure and techniques developed to evaluate the health of ocean turbines, based on vibration measurements and analyses. A LabVIEW model for on-line vibration condition monitoring, implemented with advanced diagnostic techniques features, was developed. In order to distinguish between a vibration amplitude change due to a developing fault and that due to a change in operating condition, this program includes the use of an ordering technique in the frequency domain, which relates the vibration to the machine speed. Some experiments were first performed on a commercial fan to illustrate and demonstrate the fault detection capability of the monitoring and diagnostics system. To increase the reliability of the monitoring system, and to demonstrate that it can be used for monitoring a wide range of machines, a second series of vibration data collection and monitoring events was performed on a small boat with different combination (on/off status) of the engine, hydraulic pump, generator and air conditioning. This allowed for the detection of the frequency components associated with each subsystem, alone and together, and enabled the detection of mechanical faults, such as imbalance and misalignment, if they existed. For long term monitoring, the model allow for the automatic storing of raw data either periodically and/or after any deviations from normal conditions, i.e., when alerts are on. This makes it possible to follow the progress (towards an alarm condition) of any faults without saving data continuously. In this way, measurements of unexpected events may be made without the vibration engineer’s physical presence, hopefully, early fault detection and diagnosis will avoid catastrophic failure from occurring. This enables the economic and efficient health monitoring of ocean turbines as they become operational.

Copyright © 2010 by ASME

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