Abstract
This work presents an improved fault detection by reference space optimization and simultaneous feature selection applied in a manufacturing complex process of automotive pedals components. Support vector data description (SVDD) one-class classification method uses a hypersphere with the minimum volume to find an enclosed boundary containing almost all target objects. Gompertz binary particle swarm optimization algorithm (GBPSO) is applied to optimize kernel hyperparameters for SVDD and simultaneously solve the feature selection problem. In order to justify and validate the results, also the genetic algorithm (GA) and binary particle swarm optimization algorithm (BPSO) are presented to compare the performances of the three approaches in terms of the misclassification function. The experimental results showed that the proposed approach can correctly select the influencing input variables in order to achieve an efficient fault detection.
Similar content being viewed by others
References
Sikandar A (2014) Artificial bee colony optimisation-based enhanced Mahalanobis Taguchi system for classification. Int J Intell Eng Inf 2:181–194
Chun-Chin H, Mu-Chen C, Long-Sheng C (2010) Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring. Expert Syst Appl Elsevier 37:3264–3273
Keskes H, Brahama A, Lachiri Z (2013) Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electric Power Systems Research, Elsevier 151–157
Lu D, Qiao W (2014) A GA-SVM hybrid classifier for multiclass fault identification of drivetrain gearboxes. IEEE, pp 3894–3900
Bounsiar A, Madden M G (2014) Kernels for one-class support vector machines. IEEE Computer Society, pp 1–4
Khanzode V V, Maiti J (2008) Implementing Mahalanobis-Taguchi system to improve casting quality in grey iron foundry. Int J Product Qual Manag 3:444–456
Reséndiz-Flores E O, López-Quintero M E (2016) Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach. Int J Adv Manuf Technol 86:1–7
Ghasemi E, Aaghaie A, Cudney E A (2015) Mahalanobis Taguchi system: a review. Int J Qual Reliab Manag 32:1–26
Yu-ping G, Long-sheng G, Xiang-lai C (2014) Optimization on reference space of Mahalanobis-Taguchi system based on hybrid encoding genetic algorithms. In: International conference on management science & engineering, pp 62–68
Su C, Hsiao Y (2009) Multiclass MTS for simultaneous feature selection and classification. IEEE Trans Knowl Data Eng 21: 192–205
Nunes I, Hernane D, Andrade R, Bartocci L, dos-Reis S (2017) Artificial neural networks: a practical course. Springer, Berlin
Ren J (2012) ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging. Strathprints Institutional Repository 26:144–153
Ganganwar V (2012) An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng 2:42–47
Wang D, Tan X (2013) Neural information processing. Springer, Berlin
Lei L, Xiao-Dan W, Xi L, Ya-Fei S (2015) Hierarchical error-correcting output codes based on SVDD. Pattern Anal Appl, Springer 19:163–171
Shen F, Song Z, Zhou L (2013) Improved PCA-SVDD based monitoring method for nonlinear process. In: 25th Chinese control and decision conference. IEEE, pp 4330–4336
Deng N, Tian Y, Zhang C (2013) Support vector machines, optimization based theory, algorithms, and extensions. Chapman
Yin G et al (2014) Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure. Neurocomputing, Elsevier 128:224–231
Liu J, Sun Y (2013) Multivariate statistical process monitoring scheme with PLS and SVDD. In: International conference on industrial engineering and engineering management, pp 57–70
Tax D M J, Duin R P W (2004) Support vector data description. Mach Learn 54:45–66
Lin S et al (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8:1505–1512
Mu T, Nandi A K (2009) Multiclass classification based on extended support vector data description. IEEE Trans Syst Man Cybern 39:1206–1216
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942–1948
Reséndiz E, Rull-Flores C A (2013) Mahalanobis-Taguchi system applied to variable selection in automotive pedals components using Gompertz binary particle swarm optimization. Expert Syst Appl 40:2361–2365
Haixiang G, Yijing L, Yanan L, Xiao L, Jinling L (2016) BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Eng Appl Artif Intell 49: 176–193
Assareh E, Behrang M A, Assari M R, Ghanbarzadeh A (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Elsevier 35:5223–5229
Hassan R, Cohanim B, de-Weck O (2004) A comparison of particle swarm optimization and the genetic algorithm. American Institute of Aeronautics and Astronautics, pp 1–13
Alkindy B, Al-Nuaimi B, Guyeux C, Couchot J, Salomon M, Alsrraj R, Philippe L (2016) Binary particle swarm optimization versus hybrid genetic algorithm for inferring well supported phylogenetic trees. Springer International Publishing, Switzerland, pp 165–179
Lhotská L, Macaš M, Burša M (2006) PSO and ACO in optimization problems. Intelligent Data Engineering and Automated Learning 4224:1390–1398
Pal S K, Rai C S, Singh A P (2012) Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. I. J Intell Syst Appl 10:50–57
Sadeghi J, Mousavi S M, Niaki S T, Sadeghi S (2013) Optimizing a multi-vendor multi-retailer vendor managed inventory problem: two tuned meta-heuristic algorithms. ScienceDirect 50: 159–170
Mousavi S M, Hajipour V, Niaki S T, Aalikar N (2014) A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm. Int J Adv Manuf Technol 70:1739–1756
Mousavi S M, Bahreininejad A, Musa S N, Yusof F (2014) A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Int J Adv Manuf Technol 28:191–206
Tavana M, Li Z, Mobin M, Komaki M (2016) Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Syst Appl 50:17–39
Mousavi S M, Sadeghi J, Niaki S T, Alikar N, Bahreininejad A, Metselaar H S (2014) Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment. Inf Sci 276:42–62
Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics, pp 4104–4108
Khanesar M A, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: Mediterranean conference on control and automation
Das S, Abraham A, Konar A (2008) Particle swarm optimization and differential evolution algorithms. Technical analysis, applications and hybridization perspectives, studies in computational intelligence, vol 116, pp 1–38
Deep K, Chauhan P, Pant M (2012) Multi task selection including part mix, tool allocation and process plans in CNC machining centers using new binary PSO. In: IEEE world congress on computational intelligence, pp 10–15
Holland J H (1975) Adaptive in natural and artificial systems. University of Michigan, Ann Arbor
Bhajantri L B, Nalini N (2014) Genetic algorithm based node fault detection and recovery in distributed sensor networks. IJ Comput Netw Inf Secur 12:37–46
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn Elsevier 33:1455–1465
Gamarra M R, Quintero C G (2013) Using genetic algorithm feature selection in neural classification systems for image pattern recognition. Ingeniería e Investigación 1:52–58
Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res Elsevier 35:3202–3212
Yan X, Liu H, Zhu Z, Wu Q (2016) Hybrid genetic algorithm for engineering design problems. J Comput Theor Nanosci 13:6312–6319
Kim J W, Kim S K (2016) Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions. J Supercomput Springer 72:3549–3571
Giaouris D, Finch J W, Ferreira O C, Kennel R M, El-Murr G (2008) Wavelet denoising for electric drives. IEEE Trans Ind Electron 55:543–550
Aminghafari M, Cheze N, Poggi J M (2006) Multivariate denoising using wavelets and principal component analysis. Comput Stat Data Anal 50:2381–2398
Pal A, Maiti J (2010) Development of a hybrid methodology for dimensionality reduction in Mahalanobis–Taguchi system using Mahalanobis distance and binary particle swarm optimization. Expert Syst Appl 37:1286–1293
Neumann F, Witt C (2010) Bioinspired computation in combinatorial optimization. Natural Computing Series, vol 2. Springer, Berlin, pp 9–19
Reséndiz E, Moncayo-Martínez LA, Solís G (2013) Binary ant colony optimization applied to variable screening in the Mahalanobisaguchi System. Expert Syst Appl 40:634–637
Karlis D, Saporta G, Spinakis A (2003) A simple rule for the selection of principal components. Commun Stat Theory Methods 32:643–666
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Navarro-Acosta, J.A., Reséndiz-Flores, E.O. Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components. Int J Adv Manuf Technol 92, 2311–2324 (2017). https://doi.org/10.1007/s00170-017-0333-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-017-0333-y