Journal of Applied and Computational Mechanics

Prognosis and Detection of Experimental Failures in Open Field Diesel ‎Engines Applying Wiener's Artificial Immunological Systems

Document Type : Research Paper

Authors

1 Faculty of Technology of Araçatuba, Department of Biofuels, Av. Prestes Maia, 1764 - Ipanema, Araçatuba, 16052-045, Brazil

2 UNESP - Instituto de Química, Department of Engenharia, Física e Matemática, Rua Prof. Francisco Degni, 55 – Quitandinha,‎ Araraquara, 14800-060, Brazil‎

3 Faculty of Technology of Catanduva, Department of Automação Indusrial, Av. Rua Maranhão, 898 - Centro, Catanduva, 15800-020, Brazil‎

4 UNESP - Univ. Estadual Paulista, Faculty of Engineering of Ilha Solteira, Department of Mechanical Engineering, Ilha Solteira, 15385-000, Brazil‎

5 UNESP - Univ. Estadual Paulista, Faculty of Engineering of Ilha Solteira, Department of Mechanical Engineering,‎ Ilha Solteira, 15385-000, Brazil‎

Abstract

The high costs of open-field diesel engines arise from the lack of maintenance of these systems. Thus, the maintenance of this equipment has been treated as a great challenge, as some methods of data monitoring are not possible to be implemented, given the inadequate sensing conditions, plant location, local climate, facilities, even the methods and maintenance routines. In a second step, the labor is not qualified and of sufficient quantity to meet the demand, resulting in a slow and inefficient system. One of the challenges of predictive systems is to inform damage and failures in real time of the operating conditions of these machines and equipment. This work demonstrates the possibility of analyzing and detecting failures in open field predictive systems, using the concepts of vibration and acoustics in artificial intelligence. One of the results of this work demonstrates the robustness of the negative selection artificial immune system algorithm, whose application of the Wiener filter was of fundamental need. The other result demonstrates the versatility of conditioned use both or just one of the concepts between vibration and acoustics, in prognosis and fault detection. Considering the versatility of using these two techniques, it is possible to affirm that, the predictive systems of real time analysis have an effective solution directed to the area and, if implemented, it is of low cost and high efficiency.

Keywords

Main Subjects

[1] Sacomano, J.B., Gonçalves, R.F., Da Silva, M.T., Bonila, S.H., Sátyro, W.C., Industria 4.0: Conceitos e Fundamentos, São Paulo, Editora Blucher, 2018.
[2] Wang, L., Gao, R.X., Condition Monitoring and Control for Intelligent Manufacturing, London, Springer-Verlag, 2006.
[3] Holman, J.O., Experimental Methods for Engineers, 8 ed., New York, McGraw-Hill, 2012.
[4] Hussain, Z.M., Sadik, A.Z., O’shea, P., Digital Signal Processing: An Introduction with MATLAB and Applications, Berlin, Springer-Verlag, 2011.
[5] Yadav, G.V.P.C.S., Krishna, B.A., Kamaraju, M., Performance of Wiener Filter and Adaptive Filter for Noise Cancellation in Real-Time Environment, International Journal of Computer Applications, 97(15), 2014, 16-23.
[6] Chatzi, E.N., Papadimitriou, C., Identification Methods for Structural Health Monitoring, Switzerland, Springer, 2016.
[7] Boller, C., Chang, F.K, Fujino, Y., Encyclopedia of Structural Health Monitoring, West Sussex, John Wiley, 2009.
[8] Balageas, D., Fritzen, C.P., Guemes, A., Structural Health Monitoring, Newport Beach, ISTE, 2006.
[9] Thorby, D., Structural Dynamics and Vibration in Practice: An Engineering Handbook, Burlington, Elsevier, 2008.
[10] Dasgupta, D., Artificial Immune Systems and their Applications, Heidelberg, Springer-Verlag, 1999.
[11] Lund, O., Nielsen, M., Lundegaard, C., Kesmir, C., Brunak, S., Immunological Bioinformatics, Massachusetts Institute of Technology - MIT Press, Massachusetts, USA, 2005.
[12] Cohen, I.R., Real and artificial immune systems: computing the state of the body, Nature Reviews Immunology, 7, 2007, 569-574.
[13] Dasgupta, D., Niño, L.F., Immunological Computation: Theory and Applications, Boca Raton, Taylor & Francis Group, 2009.
[14] Zhang, J., Hou, Z., Application of Artificial Immune System in Structural Health Monitoring, Journal of Structures, 2014, 2014, 1-14.
[15] Outa, R., Chavarette, F.R., Mishra, V.N., Gonçalves, A.C., Roefero, L.G.P., Moro, T.C., Prognosis and Fail Detection in a Dynamic Rotor Using Artificial Immunological System, Engineering Computations, 2020. DOI: 10.1108/EC-08-2019-0351.
[16] Kuo, H.L., A More Generalized Wiener Filtering Technique, IFAC Proceedings Volumes, 13(11), 1980, 539-542.
[17] Poularikas, A.D., Ramadan, Z.M., Adaptive Filtering Primer with Matlab, Boca Raton, CRC Press, 2006.
[18] Daniele, V.G., Zich, R.S., The Wiener-Hopf Method in Electromagnetics, Edison, SciTech Publishing, 2014.
[19] Kailath, T., Lectures on Wiener and Kalman Filtering, Wien, Springer-Verlag, 1981.
[20] Tan, Y., Anti-Spam Techniques Based on Artificial Immune System, Boca Raton, CRC Press, 2016.
[21] De Castro, L.N., Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, Boca Raton, CRC Press, 2007.
[21] Forrest, S., Perelson, A.S., Allen, L., Cheukuri, R., Self-Nonself Discrimination in a Computer, IEEE Computer Society Symposium on Research in Security and Privacy, 1994.
[22] Bradley, D.W., Tyrrell, A.M., Immunotronics-novel finite-state-machine architectures with built-in self-test using self-nonself differentiation, IEEE Transactions on Evolutionary Computation, 6, 2002, 227-238.
[23] Lima, F.P.A., Lotufo, A.D.P., Minussi, C.R., Artificial Immune Systems Applied to Voltage Disturbance Diagnosis in Distribution Electrical Systems, Proceedings on IEEE PowerTech-2013, 2013.
Journal of Applied and Computational Mechanics
Volume 7, Issue 1
January 2021
Pages 1-12