Abstract
Fault detection and diagnosis of pneumatic valve used in cooler water spray system in cement industry is of great practical significance and paramount importance for the continued operation of the plant. This paper presents the design and development of Artificial Neural Network (ANN) based model for the fault detection of pneumatic valve in cooler water spray system in cement industry. Principal component analysis (PCA) is applied to reduce the input dimension. The training and testing data required for the development of ANN is generated in a laboratory grade experimental setup. The performance of the developed model is compared with the network trained with the original variables without any dimensionality reduction. From the comparison it is observed that the classification performance of the neural network has been improved due to the application of PCA and the training time of the neural network is reduced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bayir, S.R., Bay, O.F.: Serial wound starter motor faults diagnosis using artificial neural network. In: IEEE ICM 2004, Istanbul, pp. 194–199 (2004)
Chang, Y.-W., Wang, Y.-C., Liu, T., Wang, Z.-J.: Fault diagnosis of a mine hoist using PCA and SVM techniques. Int. J. China Univ. Mining and Technology 18, 327–331 (2008)
Demetgul, M.: Experiment based fault diagnosis on bottle filling plant with LVQ artificial neural network algorithm. Journal of Engineering Science 14, 92–100 (2008)
de Moura, E.P., Souto, C.R., Silva, A.A., Irmao, M.A.S.: Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses. Journal of Mech. Systems and Signal Processing (2010)
Devaraj, D., Murthy, N., Yegnanarayana, B.: A fuzzy system model for plant condition monitoring. In: ASME International Conference, pp. 210–214 (1999)
Isermann, R.: Supervision, fault detection and fault diagnosis methods-an introduction. Int. J. Control Engineering Practice 5(5), 639–652 (1997)
Isermann, R., Balle, P.: Trends in the application of Model-based fault detection and diagnosis of technical process. Int. J. Control Engineering Practice 5(5), 709–719 (1997)
Karpenko, M., Sepehri, N.: Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator. Engineering Applications of Artificial Intelligence 15(3-4), 273–283 (2002)
Karpenko, M., Sepehri, N., Scuse, D.: Diagnosis of process valve actuator faults using a multilayer neural network. Control Engineering Practice 11(11), 1289–1299 (2003)
Koj, J.: The Fault Sources of Pneumatic Servo-Motor-Control Valve Assembly. In: Proceedings of the III Polish Nat. Conf. on Diagnosis of Industrial Processes, Jurata, Poland, pp. 415–419 (1998)
Leonhardt, S., Ayoubi, M.: Methods of Fault diagnosis. Int. J. Control Engineering Practice 5(5), 683–692 (1997)
Louro, R.: Fault Diagnosis of an Industrial Actuator Valve. MSc Dissertation, Technical University of Lisbon, Portugal (2003)
Rajakarunakaran, S., Venkumar, P., Devaraj, D., Rao, K.S.P.: Artificial neural network approach for fault detection in rotary system. Applied Soft Computing 8, 740–748 (2008)
Seong, S.H., Hur, S., Kim, J.S., Kim, J.T., Park, W.M., Lee, U.C.: Development of diagnosis algorithm for the check valve with spectral estimations and neural network models using acoustic signals. Annals of Nuclear Energy 32, 479–492 (2005)
Bouhouche, S., Yahi, M., Bast, J.: Combined use of principal component analysis and Self organisation map for condition monitoring in pickling process. Applied Soft Computing 11, 3075–3082 (2011)
Subbaraj, P., Kannapiran, B.: Artificial Neural Network Approach for Fault Detection in Pneumatic Valve in Cooler Water Spray System. Int. Journal of Comp. Applications 9(7), 975–8887 (2010)
Venkatasubramanian, V.: A Review of Process Fault Detection and Diagnosis Part I, II & III. Computers and Chemical Engineering (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Subbaraj, P., Kannapiran, B. (2011). Neural Network Based Model for Fault Diagnosis of Pneumatic Valve with Dimensionality Reduction. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-27172-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27171-7
Online ISBN: 978-3-642-27172-4
eBook Packages: Computer ScienceComputer Science (R0)