Matches in SemOpenAlex for { <https://semopenalex.org/work/W32344561> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W32344561 abstract "Power plants are one of the most important parts of electric networks. The most important parts in the power plants are turbines, generators and power transformers. These devices are very expensive and their healthy is vitally important. Therefore diagnosis and monitoring systems for preventing catastrophic faults and also for detecting the on-going damages are really necessary and valuable. According to the expense of maintenance of turbines and transformers and sometimes spending a lot of time to inspect them inside, it is necessary to predict the fault in them before happening and without inspecting inside (nonintrusive). With on-line predictive maintenance we would be able to decrease the cost of maintenance and surly improve the performance, and also detecting the incipient faults and finally forecasting the remained life age of these devices. According to my research, the best methods for predicting faults in power transformers are on-line DGA, on-line SFRA and on-line PD method. Also one of the best on-line methods for diagnosis in turbines is vibration analysis. By mounting some acceleration transducers on the bearings of turbines, we can measure the vibration of the system and after that by doing some post processing; we can decide that the turbine's condition is normal or no. Also sometimes we can discover the origin of the vibration, for example unbalance, misalignment and so on. In this thesis, first of all I describe the main methods that I have used for writing the fault detection algorithm and then I apply the fault detection algorithm on RTR simulation model in MATLAB environment to check the validity of algorithm. After that I apply the written algorithm on a real test-bench called that it is a rotor with 3 active magnetic bearings. I measure the vibration signals of 5assi by using some displacement sensors and I check the performance of the algorithm in many cases includes normal condition and unbalance condition with different levels of unbalance. The results show that the algorithm is able to detect the unbalance problem and warn operators perfectly. At the last step I apply the algorithm on a real time monitoring system on a turbine in the power plant Pont St. Martin in the province Aosta in the north of Italy to check the performance of the algorithm in a real situation. We use eight accelerometers to measure the vibration signals and finally we generate one index for each sensor that shows the condition of the correspondent signal. With trending these indexes during the time and according to some defined alarm and trip levels, we would be able to detect almost any kind of faults in turbines before happening and warn the operators in a proper time. With using this on-line monitoring system, we would be able to save a lot of money and time and surly we can increase the performance of the power plants." @default.
- W32344561 created "2016-06-24" @default.
- W32344561 creator A5055085309 @default.
- W32344561 date "2012-01-01" @default.
- W32344561 modified "2023-09-23" @default.
- W32344561 title "AUTOMATIC ON-LINE FAULT DETECTIONALGORITHM FOR HYDRAULIC TURBINES" @default.
- W32344561 doi "https://doi.org/10.6092/polito/porto/2497636" @default.
- W32344561 hasPublicationYear "2012" @default.
- W32344561 type Work @default.
- W32344561 sameAs 32344561 @default.
- W32344561 citedByCount "0" @default.
- W32344561 crossrefType "dissertation" @default.
- W32344561 hasAuthorship W32344561A5055085309 @default.
- W32344561 hasConcept C119599485 @default.
- W32344561 hasConcept C121332964 @default.
- W32344561 hasConcept C127313418 @default.
- W32344561 hasConcept C127413603 @default.
- W32344561 hasConcept C133731056 @default.
- W32344561 hasConcept C152745839 @default.
- W32344561 hasConcept C165205528 @default.
- W32344561 hasConcept C165801399 @default.
- W32344561 hasConcept C171146098 @default.
- W32344561 hasConcept C172707124 @default.
- W32344561 hasConcept C175551986 @default.
- W32344561 hasConcept C198352243 @default.
- W32344561 hasConcept C198394728 @default.
- W32344561 hasConcept C200601418 @default.
- W32344561 hasConcept C24890656 @default.
- W32344561 hasConcept C2524010 @default.
- W32344561 hasConcept C2775846686 @default.
- W32344561 hasConcept C2778449969 @default.
- W32344561 hasConcept C33923547 @default.
- W32344561 hasConcept C41008148 @default.
- W32344561 hasConcept C66322947 @default.
- W32344561 hasConcept C70452415 @default.
- W32344561 hasConcept C78519656 @default.
- W32344561 hasConceptScore W32344561C119599485 @default.
- W32344561 hasConceptScore W32344561C121332964 @default.
- W32344561 hasConceptScore W32344561C127313418 @default.
- W32344561 hasConceptScore W32344561C127413603 @default.
- W32344561 hasConceptScore W32344561C133731056 @default.
- W32344561 hasConceptScore W32344561C152745839 @default.
- W32344561 hasConceptScore W32344561C165205528 @default.
- W32344561 hasConceptScore W32344561C165801399 @default.
- W32344561 hasConceptScore W32344561C171146098 @default.
- W32344561 hasConceptScore W32344561C172707124 @default.
- W32344561 hasConceptScore W32344561C175551986 @default.
- W32344561 hasConceptScore W32344561C198352243 @default.
- W32344561 hasConceptScore W32344561C198394728 @default.
- W32344561 hasConceptScore W32344561C200601418 @default.
- W32344561 hasConceptScore W32344561C24890656 @default.
- W32344561 hasConceptScore W32344561C2524010 @default.
- W32344561 hasConceptScore W32344561C2775846686 @default.
- W32344561 hasConceptScore W32344561C2778449969 @default.
- W32344561 hasConceptScore W32344561C33923547 @default.
- W32344561 hasConceptScore W32344561C41008148 @default.
- W32344561 hasConceptScore W32344561C66322947 @default.
- W32344561 hasConceptScore W32344561C70452415 @default.
- W32344561 hasConceptScore W32344561C78519656 @default.
- W32344561 hasLocation W323445611 @default.
- W32344561 hasOpenAccess W32344561 @default.
- W32344561 hasPrimaryLocation W323445611 @default.
- W32344561 hasRelatedWork W1702878644 @default.
- W32344561 hasRelatedWork W1709366416 @default.
- W32344561 hasRelatedWork W1970027578 @default.
- W32344561 hasRelatedWork W1974789100 @default.
- W32344561 hasRelatedWork W2008772620 @default.
- W32344561 hasRelatedWork W2025146494 @default.
- W32344561 hasRelatedWork W2042023855 @default.
- W32344561 hasRelatedWork W2057442489 @default.
- W32344561 hasRelatedWork W2085764301 @default.
- W32344561 hasRelatedWork W2094868461 @default.
- W32344561 hasRelatedWork W2107301166 @default.
- W32344561 hasRelatedWork W2113680010 @default.
- W32344561 hasRelatedWork W2116858113 @default.
- W32344561 hasRelatedWork W2162971607 @default.
- W32344561 hasRelatedWork W2291264318 @default.
- W32344561 hasRelatedWork W2300460225 @default.
- W32344561 hasRelatedWork W2771002192 @default.
- W32344561 hasRelatedWork W2909886097 @default.
- W32344561 hasRelatedWork W34224245 @default.
- W32344561 hasRelatedWork W39546076 @default.
- W32344561 isParatext "false" @default.
- W32344561 isRetracted "false" @default.
- W32344561 magId "32344561" @default.
- W32344561 workType "dissertation" @default.