Identification of mass-spring vibration system using measured vibration test data

• Main Author: Lock, Siew Min.
• Other Authors: Lin Rongming
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics
• Online Access: http://hdl.handle.net/10356/16980
• Institution: Nanyang Technological University

This report will serve to analyze the processes of identifications of system parameters of the mass-spring system (MSS) using the measurements of vibrations test data. In today’s technology, there is a need for engineers to perform vibration analysis where the machine’s fundamental frequency can be identified, so that predictive maintenances can be undertaken to prevent possible failures and malfunctioning. By modeling a machine as a simple mass-spring-damper model and identifying its necessary system parameters, changes and improvements can be made to the necessary areas of the machine. Using vibration analysis techniques for detecting and locating faults, machinery fault-related problems can be diagnosed early. Conditions of resonance can also be identified and rectifications of these problems can be done with performance evaluations. Details on how the identifications of system parameters on multiple degrees of freedom systems are carried out will be examined and discussed in the later chapter of the report. From the experimental results that were obtained, it was observed that there were discrepancies of values between that of the specifications quoted by the supplier. However, most of the results obtained actually fell within the acceptable percentage error of 25%. Of which, some sets of configurations had achieved results that were significantly close to that of the values quoted by the supplier, with the percentage error (absolute) from 5%. It was further observed that a control system has higher reliability in identifying the system parameters for single degree of freedom system, as more sets of configurations fall within the 25% error.