Artificial neural network approach for fault detection in rotary system S. Rajakarunakaran a, * , P. Venkumar a , D. Devaraj a , K. Surya Prakasa Rao b a A.K College of Engineering, Anand Nagar, Krishnankoil 626190, Tamilnadu, India b Department of Industrial Engineering, Anna University, Chennai 600025, Tamilnadu, India Received 21 June 2006; received in revised form 6 June 2007; accepted 10 June 2007 Available online 14 June 2007 Abstract The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented. # 2007 Elsevier B.V. All rights reserved. Keywords: Fault detection; Neural networks; Back propagation; Adaptive resonance theory; Rotary system 1. Introduction The problem of detecting faults in complex real plants is strategically important for its various implications, e.g., avoiding major plant breakdowns and catastrophes, safety problems, fast and appropriate response to emergency situations and plant maintenance. For instance, the following systems represent only a small part of systems where fault detection is in general a very difficult, yet important task: chemical plants, refineries, power plants, airplanes, ships, submarines, space vehicles and space stations, automobiles and household appliances. Generally, in process industries, there is a crucial need for checking and monitoring the equipment condition precisely since they are mostly subject to hazardous environments, such as severe shocks, vibration, heat, friction, dust, etc. So fault detection, fault identification and diagnosis of equipments, machineries and systems have become a vigorous area of work. Due to the broad scope of the process fault diagnosis problem and the difficulties in its real-time solution, many analytical-based techniques [1,2] have been proposed during the past several years for the fault detection of technical plants. The important aspect of these approaches is the development of a model that describes the ‘cause and effect’ relationships between the system variables using state estimation or parameter estimation techniques. The problem with these mathematical model-based techniques is that under real conditions, no accurate models of the system of interest can be obtained. In that case, the better strategy is of using knowledge-based techniques where the knowledge is derived in terms of facts and rules from the description of system structure and behaviour. Classical expert systems were used for this purpose. The major weakness of this approach is that binary logical decisions with Boolean operators do not reflect the gradual nature of many real world problems. Recently, with the development of artificial intelligence, Computational Intelli- gence (CI) methods, Neural Networks (NN), Fuzzy Logic (FL), Evolutionary Algorithms (EA), etc., more and more fault diagnostic approaches have emerged as new techniques for fault diagnostic systems [3,4]. Any complex system is liable to faults or failures. A ‘fault’ is an unexpected change of the system functionality. It manifests as a deviation of at least one characteristic property or variable of a technical process. It may not, however, represent the failure of physical components. Such malfunctions may occur either in www.elsevier.com/locate/asoc Applied Soft Computing 8 (2008) 740–748 * Corresponding author. Tel.: +91 9443868730. E-mail address: srajakarunakaran@yahoo.com (S. Rajakarunakaran). 1568-4946/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2007.06.002