Erfan Ahadi - Master of Science Student, Iran University of science and technology
Mustafa Larky - Master of Science Student, Iran University of science and technology;
Mohammad Riahi - chiar of mechanical engineering, Iran University of science and technology;

Rotating Machineries have vital importance in the performance of industries. Thus, continued monitoring of their condition is essential. On the other hand, with recent advances in manufactunng capacities of plants. machines are expected to work reliably for long periods of time without breakdowns. For this reason Prognostics and health management (PHM) has gained attention of many researchers. PHM can assist maintenance engineers to reduce costs, increase safety and reliability of machines by diagnosis of faults and finally, predicting its future status. One of the most challenging aspects of PHM is to predict remaining useful life (RUL) of machines which defines the remaining time until machine cannot perfomi as expected. Many different methods of intelligent diagnostics and prognostics of machineries are introduced by researchers. Amongst these methods using artificial intelligence (AI) has gained much attention recently, In this paper application of different methods of Al such as artificial neural network(ANN), support vector machine (SVR) and genetic algorithm (GA) for prognostics of rotating machineries are reviewed. Finally, their advantages and disadvantages, limitations, research gaps and future trends of research are delivered.

prognostics, remaining useful life, artificial intelligence. rotating machines