A New Scheme for Fault Detection and Classification Applied to DC Motor
DOI:
https://doi.org/10.5540/tema.2018.019.02.327Palavras-chave:
Fault Detection and Classification, Luenberger Observer, Particle Swarm Clustering.Resumo
This study presents an approach for fault detection and classification in a DC drive system. The fault is detected by a classical Luenberger observer. After the fault detection, the fault classification is started. The fault classification, the main contribution of this paper, is based on a representation which combines the Subctrative Clustering algorithm with an adaptation of Particle Swarm Clustering.Referências
R. Isermann and P. Balle, “Trends in the application of model-based fault detection and diagnosis of technical processes,” Control Engineering Practice, vol. 5, no. 5, pp. 707–719, 1997.
J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems, vol. 3. Springer Science & Business Media, 2012.
V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis – part I: Quantitative model-based methods,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 293–311, 2003.
V. Venkatasubramanian, R. Rengaswamy, and S. N. Kavuri, “A review of process fault detection and diagnosis – part II: Qualitative models and search strategies,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 313–326, 2003.
V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin, “A review of process fault detection and diagnosis – part III: Process history based methods,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 327–346, 2003.
W. M. Caminhas and R. H. C. Takahashi, “Dynamic system failure detection and diagnosis employing sliding mode observers and fuzzy neural networks,” in Proceedings of the Joint 9th IFSA and 20th NAFIPS, (Vancouver), pp. 304–309, 2001.
R. H. C. Takahashi and P. L. D. Peres, “Unknown input observers for uncertain systems: A unifying approach,” European Journal of Control, vol. 5, no. 2–4, pp. 261–275, 1999.
R. H. C. Takahashi, R. M. Palhares, and P. L. D. Peres, “Discrete-time singular observers: H2/H1 optimality and unknown inputs,” International Journal of Control, vol. 72, no. 6, pp. 481–492, 1999.
W. Chen and M. Saif, “Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems,” IET Control Theoty and Applications, vol. 1, no. 6, pp. 1672–1680, 2007.
V. Puig, A. Stancu, T. Escobet, F. Nejjari, J. Quevedo, and R. Patton, “Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem,” Control Engineering Practice, vol. 14, no. 6, pp. 621–633, 2006.
M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops,” Chemical Engineering Science, vol. 61, no. 6, pp. 1790–1810, 2006.
H. Cheng, M. Nikus, and S. Jamsa-Jounela, “Fault diagnosis of the paper machine short circulation process using novel dynamic causal digraph reasoning,” Journal of Process Control, vol. 18, no. 7–8, pp. 676–691, 2008.
T. Boukhobza, F. Hamelin, and S. Canitrot, “A graph-theoretic approach to fault detection and isolation for structured bilinear systems,” International Journal of Control, vol. 81, no. 4, pp. 661–678, 2008.
Y. Dutuit and A. Rauzy, “Approximate estimation of system reliability via fault trees,” Reliability Engineering & System Safety, vol. 87, no. 2, pp. 163–172, 2005.
S. M. El-Shal and A. S. Morris, “A fuzzy expert system for fault detection in statistical process control of industrial processes,” IEEE Transactions on Systems, Man and Cybernetics, Part C, vol. 30, no. 2, pp. 281–289, 2000.
J. Ragot and D. Maquin, “Fault measurement detection in an urban water supply network,” Journal of Process Control, vol. 16, no. 9, pp. 887–902, 2006.
K. P. Detroja, , R. D. Gudi, and S. C. Patwardhan, “A possibilistic clustering approach to novel fault detection and isolation,” Journal of Process Control, vol. 16, no. 10, pp. 1055–1073, 2006.
M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “Fault diagnosis using dynamic trend analysis: A review and recent developments,” Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 133–146, 2007.
K. F. Fong, A. P. Loh, and W. W. Tan, “A frequency domain approach for fault detection,” International Journal of Control, vol. 81, no. 2, pp. 264–276, 2008.
M. F. S. V. D’Angelo, R. M. Palhares, R. H. C. Takahashi, R. H. Loschi, L. M. R. Baccarini, and W. M. Caminhas, “Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach,” Applied Soft Computing, vol. 11, no. 1, pp. 179–192, 2011.
M. F. S. V. D’Angelo, R. M. Palhares, R. H. C. Takahashi, and R. H. Loschi, “Fuzzy/Bayesian change point detection approach to incipient fault detection,” IET Control Theory & Applications, vol. 5, no. 4, pp. 539–551, 2011.
S. Verron, T. Tiplica, and A. Kobi, “Fault detection and identification with a new feature selection based on mutual information,” Journal of Process Control, vol. 18, no. 5, pp. 479–490, 2008.
J. M. F. Calado, J. Korbicz, K. Patan, R. J. Patton, and J. M. G. S. da Costa, “Soft computing approaches to fault diagnosis for dynamic systems,” European Journal of Control, vol. 7, no. 2-3, pp. 248–286, 2001.
M. F. S. V. D’Angelo and P. P. Costa, “Detection of shorted turns in the field winding of turbogenerators using the neural network mlp,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, (Tucson), pp. 1930–1935, 2001.
F. J. Uppal, R. J. Patton, and M. Witczak, “A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem,” Control Engineering Practice, vol. 14, no. 6, pp. 699–717, 2006.
K. Patan and T. Parisini, “Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process,” Journal of Process Control, vol. 15, no. 1, pp. 67–79, 2005.
C. A. Laurentys, R. M. Palhares, and W. M. Caminhas, “Design of an artificial immune system based on danger model for fault detection,” Expert Systems with Applications, vol. 37, no. 7, pp. 5145–5152, 2010.
C. A. Laurentys, R. M. Palhares, and W. M. Caminhas, “A novel artificial immune system for fault behavior detection,” Expert Systems with Applications, vol. 38, no. 11, pp. 6957–6966, 2011.
G. C. Silva, R. M. Palhares, and W. M. Caminhas, “Immune inspired fault detection and diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering,” Expert Systems with Applications, vol. 39, no. 16, pp. 12474–12486, 2012.
B. G. Xu, “Intelligent fault inference for rotating flexible rotors using Bayesian belief network,” Expert Systems with Applications, vol. 39, no. 1, pp. 816–822, 2012.
S. Verron, J. Li, and T. Tiplica, “Fault detection and isolation of faults in a multivariate process with Bayesian network,” Journal of Process Control, vol. 20, no. 8, pp. 902–911, 2010.
C. A. Laurentys, C. H. M. Bomfim, B. R. Menezes, and W. M. Caminhas, “Design of a pipeline leakage detection using expert system: A novel approach,” Applied Soft Computing, vol. 11, no. 1, pp. 1057–1066, 2011.
M. F. S. V. D’Angelo, R. M. Palhares, L. B. Cosme, L. A. Aguiar, F. S. Fonseca, andW. M. Caminhas, “Fault detection in dynamic systems by a fuzzy/bayesian network formulation,” Applied Soft Computing, vol. 21, pp. 647–653, 2014.
W. M. Caminhas and R. H. C. Takahashi, “Dynamic system failure detection and diagnosis employing sliding mode observers and fuzzy neural networks,” in Joint nineth IFSA world congress and 20th NAFIPS international conference, pp. 304–309, 2001.
A. K. F. Prior and L. N. Castro, “cpsc: Um algoritmo de enxame construtivo para agrupamento de dados,” in Anais do XVIII Congresso Brasileiro de Automática, pp. 3300–3307, 2010.
L. N. d. C. Sandra C. M. Cohen, “Data clustering with particle swarms,” International Conference on Evolutionary Computation, Proceedings of World Congress on Computational Intelligence, pp. 1792–1798, 2006.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” pp. 1942–1948, Proceedings of IEEE International Conference on Neural Networks, 1995.
D. Bajer, G. Martinović, and J. Brest, “A population initialization method for evolutionary algorithms based on clustering and cauchy deviates,” Expert Systems with Applications, vol. 60, pp. 294–310, 2016.
S. L. Chiu, “Method and software for extracting fuzzy classification rules by subtractive clustering,” Biennial Conf. North American Fuzzy Information Processing Soc., pp. 461–465, 1996.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Direitos Autorais
Autores de artigos publicados no periódico Trends in Computational and Applied Mathematics mantêm os direitos autorais de seus trabalhos. O periódico utiliza a Atribuição Creative Commons (CC-BY) nos artigos publicados. Os autores concedem ao periódico o direito de primeira publicação.
Propriedade Intelectual e Termos de uso
O conteúdo dos artigos é de responsabilidade exclusiva dos autores. O periódico utiliza a Atribuição Creative Commons (CC-BY) nos artigos publicados. Esta licença permite que os artigos publicados sejam reutilizados sem permissão para qualquer finalidade, desde que o trabalho original seja corretamente citado.
O periódico encoraja os Autores a autoarquivar seus manuscritos aceitos, publicando-os em blogs pessoais, repositórios institucionais e mídias sociais acadêmicas, bem como postando-os em suas mídias sociais pessoais, desde que seja incluída a citação completa à versão do website da revista.
