| Author: | Li, Siu-cheung |
| Title: | Condition monitoring of rotating electrical machine |
| Degree: | M.Sc. |
| Year: | 1996 |
| Subject: | Electric machinery Machinery -- Monitoring Machinery -- Maintenance and repair Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies |
| Pages: | iv, 127 leaves : ill. (some col.) ; 30 cm |
| Language: | English |
| Abstract: | Rotating electrical machines are widely used in many areas. In some cases, the breakdown of these machines will have serious consequences upon the production process to result in great losses. Thus, it is very important to provide maintenance on the machine. Among the various maintenance strategies, condition monitoring maintenance seems to be the most effective maintenance method. Basically, there are four important techniques to monitor the condition of the machine. They are classified as electrical, chemical, mechanical and thermal techniques. In this dissertation, one shall confine the discussion and study the electrical and mechanical techniques by monitoring the stator current spectra and the vibration spectra picked up from ball bearings with the help of dynamic signal analyzer and vibration analyzer. The electrical technique uses the fact that current drawn by a normal motor should have a single component of supply frequency. Any defects in the rotor circuit will generate a sideband below the supply frequency, with the former displaced from the latter by twice the slip frequency. For the mechanical technique, the vibration spectrum of the motor will be recorded by the analyzer. Any defects in the rotor and stator circuits will result in increases in the amplitude of vibration and the generation of sidebands in the vicinity of the supply frequency and its harmonics. The dissertation describes the process of inputting the vibration spectra into an artificial neural network in studying the health status of the motor. Although some of the results are inconsistent with the expected result due to the speed variation of the test motor, we can still demonstrate that one can use artificial neural network to analyze the condition of motor without much skill and difficulty. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b12317913.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.08 MB | Adobe PDF | View/Open |
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