Eng. & Tech. Journal ,Vol.32, Part (A), No.8, 2014 1931 Support Vector Machines for Predicting The Electrical Faults Dr. Tarik Rashid Engineering College, University of Salahaddin/Erbil Email: tarikrashid4@gmail.com Salar J. Abdulhameed Engineering College, University of Salahaddin/Erbil salar_atroshi@yahoo.com Received on: 31/10/2013 & Accepted on: 6/4/2014 Abstract Support vector machines (SVMs) are a non-probabilistic binary linear classifier in machine learning techniques and are supervised learning algorithms that classify, predict, recognise and analyse patterns. This technique was developed in early 1990s.Training algorithms of support vector machines help build a model that assigns new examples into one class or the other when a set of training examples is recycled in the training process. This feature in SVM has attracted many of researchers to develop SVM methods and their applications. In this paper work support vector machines are used to tackle electrical faults in single phase circuits. Support vectors machines are evaluated against Simple Linear Regression techniques. Support vector machines outperformed Simple Linear Regression techniques. Keywords: Support Vector Machines, A simple Linear Regression technique, Electrical Fault Perdition. اﻟﻜﮭﺮﺑﺎﺋﯿﺔ ﺑﺎﻻﻋﻄﺎل ﻟﻠﺘﻨﺒﻮء اﻟﻤﺘﺠﮫ دﻋﻢ ﻣﺎﻛﯿﻨﺎت اﻟﺨﻼﺻﺔ: وﺗﻌﺘﺒﺮ اﻟﻤﺎﻛﻨﺔ ﺗﻌﻠﯿﻢ ﻓﻲ اﻻﺣﺘﻤﺎﻟﯿﺔ ﻏﯿﺮ اﻟﺜﻨﺎﺋﯿﺔ اﻟﻤﺼﻨﻔﺎت ﻣﻦ اﻟﻤﺘﺠﮫ دﻋﻢ ﻣﺎﻛﯿﻨﺎت ﺗﻌﺘﺒﺮ اﻧﻮاع ﻣﻦ اﻻﺻﻨﺎف وﺗﺤﻠﻞ وﺗﻤﯿﺰ وﺗﺘﻨﺒﺄ ﺗﺼﻨﻒ واﻟﺘﻲ اﻟﻤﺸﺮف ﻋﻠﻰ اﻟﻤﻌﺘﻤﺪة اﻟﺨﻮارزﻣﯿﺎت. طﻮرت ﻋﺎم ﺑﺪاﯾﺎت ﻓﻲ اﻟﺘﻘﻨﯿﺔ ھﺬه1990 . ﻧﻤﻮذج ﺑﻨﺎء ﻓﻲ ﺗﺴﺎﻋﺪ اﻟﻤﺎﻛﯿﻨﺎت ﻟﮭﺬه اﻟﺘﺪرﯾﺐ ﺧﻮارزﻣﯿﺎت اﻟﺘﺪرﯾﺐ ﻣﺮﺣﻠﺔ ﻓﻲ اﻻﻣﺜﻠﺔ أﻋﺎدة ﺗﺘﻢ ﻋﻨﺪﻣﺎ أﻛﺜﺮ أو واﺣﺪ ﻟﺼﻨﻒ ﺟﺪﯾﺪة أﻣﺜﻠﺔ ﯾﺨﺼﺺ. اﻟﺨﺎﺻﯿﺔ ھﺬه ﺗﺴﺘﻘﻄﺐ وﺗﻄﺒﯿﻘﺎﺗﮭﺎ اﻟﻤﺘﺠﮫ دﻋﻢ ﻣﺎﻛﯿﻨﺎت طﺮق ﻟﺘﻄﻮﯾﺮ ﺑﺎﺣﺜﯿﻦ ﻋﺪة. اﺳﺘﺨﺪام ﺗﻢ اﻟﺒﺤﺚ ھﺬا ﻓﻲ اﻟﻮاﺣﺪ اﻟﻄﻮر دواﺋﺮ ﻓﻲ اﻟﻜﮭﺮﺑﺎﺋﯿﺔ اﻻﻋﻄﺎل ﻟﺘﺸﺨﯿﺺ اﻟﻤﺘﺠﮫ دﻋﻢ ﻣﺎﻛﯿﻨﺎت. ﻣﺎﻛﯿﻨﺎت أداء ﺗﻘﯿﯿﻢ ﺑﻌﺪ ﺗﻘﻨﯿﺔ ﻋﻠﻰ اﻟﻤﺘﺠﮫ دﻋﻢ ﻣﺎﻛﯿﻨﺎت ﺗﻔﻮﻗﺖ اﻟﺒﺴﯿﻂ، اﻟﺨﻄﻲ اﻻﻧﺤﺪار ﺗﻘﻨﯿﺔ ﻣﻊ ﺑﺎﻟﻤﻘﺎرﻧﺔ اﻟﻤﺘﺠﮫ دﻋﻢ اﻟﺒﺴﯿﻂ اﻟﺨﻄﻲ اﻻﻧﺤﺪار.