Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The...
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| Main Authors: | , , , |
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| Format: | Article |
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Maxwell Scientific Publication Corporation
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/68489/ http://dx.doi.org/10.19026/rjaset.12.2 687 |
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| Summary: | Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The protection of steam turbine generators from danger leads to reduced maintenances and augmented stability of power generation. The main proposal of this study is to identify and classify vibrations in alarm and shutdown zones, it is also intended to produce a smooth signal that can be used to adjust control value, which influences the vibration value during the start-up and power generation. We compared the series and parallel-connected Neural Network (N N) that is related to time and error to identify vibration acceleration signals and flow by sleep fuzzy sugeno s ystem, which are designed and simulated in MATLAB. The results showed that parallel-connected NN is superior to its series-connected counterpart with vibration signals, where the Neural-Sleep-Fuzz y system and the NSFS robust system produces zero voltages when it lacks vibrations, more so after receiving ali near signal to influence nonlinear signals of vibration. This study concluded that the Artificial Intelligent (AI) syste m with sleep fuzzy sugeno system can be implementing to classify the fault of optimal vibration signal limitation an d check the suitable treatment for this fault. Also, the analysis of results can conclude that using parallel NN is fast er and more accurate compared to series NN connection. |
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