28-01-2009, 01:50 PM
ABSTRACT
A laser-based contact less displacement measurement system is used for data acquisition to analyze the mechanical vibrations exhibited by vibrating structures and machines. The analysis of these vibrations requires a number of signal processing operations which include the determination of the system conditions through a classification of various observed vibration signatures and the detection of changes in the vibration signature in order to identify possible trends. This information is also combined with the physical characteristics and contextual data (operating mode, etc.) of the system under surveillance to allow the evaluation of certain characteristics like fatigue, abnormal stress, life span, etc., resulting in a high level classification of mechanical behaviors and structural faults according to the type of application.
Smart sensors or latest generation sensors are now use for vibration measurements. Where the first generation sensors are piezoelectric accelerometers, second generation sensors are modification of piezoelectric accelerometers and latest are the smart sensors. Third-generation smart sensors use mixed mode analogue and digital operations to perform simple unidirectional communication with the condition monitoring equipment.
INTRODUCTION
The study of vibrations generated by mechanical structures and electrical machines are very important. The advent of machines and processes that are more and more complex and the ever increasing exploitation and production costs have favored the emergence of several application fields requiring vibration analysis. Among these application fields, we find machine monitoring, modal analysis, quality control, and environment tests. These functions are used in fields such as aeronautics, space industry, automotive industry, energy production, civil engineering, and audio equipment.
The signal processing application described here uses a laser-based vibrometer in order to analyze the vibrations exhibited by mechanical systems. This technique can be used in the numerous applications mentioned above. The problem is to develop an intelligent system that has the ability to determine the system conditions based on a classification of the possible vibration signatures, detect changes in the vibration signature, and analyze their trends.
The classification of the various possible vibration signatures requires a priori knowledge of the mechanical system under healthy conditions as well as for the various fault conditions; when possible a mathematical model of the system should be provided. The latter is often crucial for the good interpretation of the observations, since it predicts the dynamic behavior of the structure and thus the healthy vibration signature.
Vibration spectra are in general ?peaky? due to either the periodic nature of the system?s excitation or to the natural resonance properties of the mechanical system. Changes in a vibration signal can result from a variation of the amplitude, frequency, and/or phase of one or many of the components. Moreover, new peaks may add to the existing spectrum, or some peaks may fade out. Changes can also appear in the form of short transients or spikes in the time domain. At the extreme, if the vibrations become so strong that the structure actually starts to move, then the overall average level of vibration would change, that is, a DC component would appear.
All of the above changes may occur gradually, like fatigue stress slowly deteriorating the material?s properties, or they may occur suddenly, like the rupture of a mechanical part within a machine. They may also occur periodically or in a random fashion depending on the process generating the vibrations. For multiple state systems, changes must be interpreted carefully. For example, if the operating speed of a rotating machine is raised from A to B, the vibration analysis system should not declare the observed changes as being the result of a mechanical failure, but should adapt itself to this new mode of operation.
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