Gas Turbine Bearing and Vibration Classification of Using Multi-layer Neural Network Moneer Ali Lilo 1 , Latiff, L.A* 1 , Aminudin Bin Haji Abu 2, Yousif I. Al Mashhadany 3 , Abidulkarim K. Ilijan 1 1 Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur Malaysia. 2 Malaysia - Japan International Institute of Technology, Universiti Teknologi Malaysia. Kuala Lumpur, Malaysia. 3 Engineering Department, Engineering College, University of Anbar, Iraq. E-mail:moneerlilo@yahoo.com; *Liza.kl@utm.my Abstract— 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. Keywords— Neural Network; vibration identification; vibration classification; gas turbine. I. INTRODUCTION GT is a system that produces a massive torque with a flexible speed adjustment that rotates the coupled electrical energy generator. It is also a distinguished technology due to numerous concerns such as reliability, availability, operating costs, maintenance costs, and low CO2 emissions, principally when used in a combined cycle, which is why they are commonly used in the industry[1][2] [3]. Analyses of vibration in gas turbines are considered as one of the most challenging preventive maintenance. Methods of vibration analysis and systems for condition monitoring of this equipment are under rapid development since the previous decade. Mostly, two kinds of the vibration measurement were used in GT; peak-to- peak and absolute vibration, based on the Root Mean Square (RMS) value. Indeed, vibration monitoring data can be collected from one of the following probes; velocity pickup detectors and accelerometers, both of which are typically fixed to the casing of the turbine [4]. Sensor fault detection has garnered extensive recent attention in numerous engineering areas, due to the profits of decreasing downtime, productivity loss, increasing the guarantee of safety, reliability, and quality of the system. Artificial Neural Networks (ANNs) have been extensively reported in the context of fault detection. Specifically, multilayer perception nervous networks were trained using back- propagation BP for classification purposes. ANN-BP realized very high success rates for the identification and classification machine faults. Other methods include probabilistic neural network (PNNs), which detect faults on gas turbines, were also taken into account. Principal component analysis detects sensor faults based on the utilization of the Squared Prediction Error SPE[5]. NNs are classified based on the supervision and unsupervised techniques, moreover, NNs use a different strategy to collect error feedback from the output to other stages. Clearly, BP used the error output as feedback to all of the stages. Other type are Dynamic neural networks, which are a type of neural network, where one employs an internal feedback between the input and output [6]. The intentions of this research are categorized into three parts; the first is a comparison between the series and parallel NNs, the second is an identification of the sensor number and classify the vibration level for that sensor, and the last is a design of multi-stage NNs with sleep mode for classification stage. This paper will illustrate in Section II related work for other investigators , followed by methodology of identify the vibration data based on the NN and architectures of the NN in section III, and section IV will present and discuss the results utilizing MATLAB program based on the neural algorithm ,eventually, Section V will show conclusion on the investigate done. II. RELATED WORK In recent years, the investigators began augmenting the accuracy of classifying and identifying a default on a machine. They utilized many methods to realize those objectives, such as fuzzy, neural, and wavelets [7] [8]. Moreover, the researchers start to identify the problem, and decide to minimize damage, culminating in them utilizing a hybrid method to realize the aforementioned objectives. The hybrid are termed fuzzy-neural and fuzzy-wavelet [9] [10]. This part will involve researchers employing NN, with one of them being (Zhang et al, 2008), who discussed the classification and identification vibration of thermal turbine based on the probability of a neural network PNN. The power of the spectrum, singular spectrum, and spectrums based on wavelet energy were inputted into the PNN. The accuracy in these networks is 100% in the context of classification [11]. *Corresponding author U.S. Government work not protected by U.S. copyright U.S. Government work not protected by U.S. copyright 20