Gas Turbine bearing and vibration classification of using multi-layer Neural Network

Gas Turbine (GT) is a vital component to a power plant. This system contains many signals that are used to control and protect the GT from damage or accidents caused by vibration, speed, and temperature. Moreover, the vibrations of GT at dangerous levels might lead to damages to the system. In this paper, a concerted effort is made to identify the number of the bearing and vibration levels during operations. We designed and compared two types of the Neural Networks (NNs); series and parallel NNs. They are based on the two stages from NN's employed by MATLAB. The results indicated that the parallel NN is better, depending on the time training and the produced error. Moreover, the two stages of NNs can identify the bearing number and vibration situations. The structure of the NNs puts the system in sleep mode until the vibration is in high level, however, sleeping system leads to the reduction of power consumption when designing the hardware system.