VIBRATION‐BASED DIAGNOSTIC OF STEAM TURBINE FAULTS USING EXTREME LEARNING MACHINE DHULFIQAR MOHAMMED 1 , FIRAS B. ISMAIL 2 , YAZAN ALJEROUDI 3,* ͳ Master student in Universiti Tenaga Nasional, Malaysia ʹ Senior lecturer, Department of Mechanical Engineering, Universiti Tenaga Nasional, Malaysia ͵ PhD student, )nternational )slamic University of Malaysia * Corresponding Author: yazan.aljeroudi@gmail.com ABSTRACT Automatic detection of faults and accurate diagnostic of them is very critical task in turbo machinery. Vibration signal is one rich source of information about turbo machinery conditions. Steam Turbine ST is one example of high‐complicated tubro‐ machinery process where a mathematical modeling of its components is not easy. In this context, we propose an Artificial Neural Network ANN based model for condition monitoring of ST based on vibration signal. Four High Pressure HP vibration variables for rotor in ST are analysed. Fault trends are inserted in the same time interval. Time domain shapelet features are extracted, and used to train one hidden layer, feed‐forward, ANN using Extreme Learning Machine ELM training method. The outcome is a condition‐monitoring model based on artificial neural network ANN. Root Square Mean Error (RMSE) is reported as a validation measures for different neurons numbers, and activation functions. ELM based neural network showed a convergence toward less than ͳͲ% RMSE for more than ͸Ͳ neurons in hidden layer. Keywords: Fault diagnostic; Vibration; Steam Turbine (ST); Artificial Neural Network (ANN); Extreme Learning Machine (ELM); Classification 1. INTRODUCTION A fault diagnostic system can be defined as the system that includes the capability of detecting, isolating, and identifying the fault [ͳ], [ʹ].Fault monitoring and diagnostic of rotary machinery is a quite important task. Typically, carrying this task is difficult and costly because it requires human being in the loop of production. Therefore, it is very essential for reducing the dependence of labors to have an automated fault detection and identification. )n other words, the goal of any fault detection and identification system is to replace experts by a fully automated diagnostic system. Fault diagnostic can increase the efficiency of the system functionality, reduce the cost, and avoid the possible damage that might result from the system shutdown or any caused catastrophe. Power plantsare very dependent on fault diagnostics systems. Because they are designed in a way that it should allow for working without any interrupt; any shutdown due to a sudden failure can cause serious economical damage. Moreover, this type of generator cannot be stopped temporary for any inspection. Therefore, an automated intelligent diagnostic system has to be developed and enabled to work simultaneously while machines are generating power [͵]. )n this context, we propose an Artificial Neural Network ANN based model for condition monitoring of ST based on vibration signal. Four (igh Pressure (P vibration variables for rotor in ST are analyzed. Fault trends are inserted in the same time interval. Time domain shapelet features are extracted, and used to train one hidden layer, feed‐ forward, ANN using Extreme Learning Machine ELM training method. The outcome is a condition‐monitoring model based on artificial neural network ANN. Root Square Mean Error ȋRMSEȌ is reported as a validation measures for different neurons numbers, and activation functions. All result data are discussed with future work and recommendations. The rest of the paper is organized as following: Section )) introduces reviews of state‐of‐the‐art literature of fault diagnostic using ANN methods. Our methodology is presentedin section ))). Results are given and discussed in section )V. Section V contains conclusion and future work. 2. RELATED WORKS The literature of turbo‐machinery diagnostic systems is full with different techniques. Traditional diagnostic of faults is developed using the concept of signal processing in time, frequency, or both domains [Ͷ], [ͷ]. Neural Network is very powerful technique to perform fault diagnostic of rotary machinery comparing with other approaches such statistical based techniques [͸], signal processing based [͹]. ANN attracts researchers for diagnostic application due to two factors. The first one is the learning ability. The second one is its capability to handle the non‐linearity in the real world diagnostic problems. See [ͺ].)n [ͻ]multi‐layer feed‐forward neural network for detecting of rotor propagating cracks in rotary machinery based on vibration data was presented. The neural network is trained by using vibration data with/without propagating cracks. )n this approach it was proven that simple two‐layer feed‐forward neural network is capable of identifying propagating crack while three‐ layers feed‐forward neural‐network is capable of detecting DHULFIQAR MOHAMMED et al. DATE OF PUBLICATION: DEC 12, 2014 ISSN: 2348-4098 VOL 2 ISSUE 8 NOV-DEC 2014 INTERNATIONAL JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY- www.ijset.in