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Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

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Abstract

This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor’s optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem.

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References

  • Ahandani MA (2014) A diversified shuffled frog leaping: an application for parameter identification. Appl Math Comput 239:1–16

    MathSciNet  MATH  Google Scholar 

  • Altaf S, Al-Anbuky A, Hosseini HG (2014) Fault signal propagation in a network of distributed motors. In: Proceedings of IEEE 8th international power engineering and optimization conference (PEOCO2014), Langkawi, The Jewel of Kedah, Malaysia, pp 59–63

  • Azam M, Pattipati K, Patterson-Hine A (2004) Optimal sensor allocation for fault detection and isolation. IEEE Int Conf Syst Man Cybernet 2:1309–1314

    Google Scholar 

  • Bafroui HH, Ohadi A (2014) Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 133:437–445

    Article  Google Scholar 

  • Bagajewicz M, Fuxman A, Uribe A (2004) Instrumentation network design and upgrade for process monitoring and fault detection. AIChE J 50(8):1870–1880

    Article  Google Scholar 

  • Barati M, Farsangi MM (2014) Solving unit commitment problem by a binary shuffled frog leaping algorithm. IET Gener Transm Dis 8(6):1050–1060

    Article  Google Scholar 

  • Bhushan M, Rengaswamy R (2000) Design of sensor location based on various fault diagnostic observability and reliability criteria. Comput Chem Eng 24(2–7):735–741

    Article  Google Scholar 

  • Cao HR, Niu LK, He ZJ (2012) Method for vibration response simulation and sensor placement optimization of a machine tool spindle system with a bearing defect. Sensors 12(7):8732–8754

    Article  Google Scholar 

  • Casillas MV, Puig V, Garza-Castanón LE, Rosich A (2013) Optimal sensor placement for leak location in water distribution networks using genetic algorithms. Sensors 13(11):14984–15005

    Article  Google Scholar 

  • Chen Y, Wen JY, Jiang L, Cheng SJ (2013) Hybrid algorithm for dynamic economic dispatch with valve-point effects. IET Gener Transm Dis 7(10):1096–1104

    Article  Google Scholar 

  • Cheng SF, Azarian MH, Pecht MG (2010) Sensor systems for prognostics and health management. Sensors 10(6):5774–5797

    Article  Google Scholar 

  • Chow HM, Lam HF, Yin T, Au SK (2011) Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating. Struct Control Health 18(3):305–320

    Article  Google Scholar 

  • Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Res Pl-Asce 129(3):210–225

    Article  Google Scholar 

  • Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2):129–154

    Article  MathSciNet  Google Scholar 

  • Fu KC (2009) Redundant instruments placement using ACO. In: Proceedings of the 2009 international conference on computational intelligence and natural computing (CINC2009), pp 151–154

  • Hamilton A, Cleary A, Quail F (2014) Development of a novel wear detection system for wind turbine gearboxes. IEEE Sens J 14(2):465–473

    Article  Google Scholar 

  • Han T, Yang B, Lee JM (2005) A new condition monitoring and fault diagnosis system of induction motors using artificial intelligence algorithms. In IEEE international conference on electric machines and drives, pp 1967–1974

  • Hang J, Zhang JZ, Cheng M (2014) Fault diagnosis of wind turbine based on multi-sensors information fusion technology. IET Renew Power Gener 8(3):289–298

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, USA

    Google Scholar 

  • IEEE STD 1522-2004. IEEE trial use standard testability and diagnosability characteristics and metrics, Piscataway, NJ: IEEE Standards Press

  • Jazebi S, Haji MM, Naghizadeh RA (2014) Distribution network reconfiguration in the presence of harmonic loads: optimization techniques and analysis. IEEE Trans Smart Grid 5(4):1929–1937

    Article  Google Scholar 

  • Jin S, Liu YH, Lin ZQ (2012) A Bayesian network approach for fixture fault diagnosis in launch of the assembly process. Int J Prod Res 50(23):6655–6666

    Article  Google Scholar 

  • Kavousi-Fard A, Akbari-Zadeh M-R (2013) Reliability enhancement using optimal distribution feeder reconfiguration. Neurocomputing 106:1–11

    Article  Google Scholar 

  • Korkali M, Abur A (2013) Optimal deployment of wide-area synchronized measurements for fault-location observability. IEEE Trans Power Syst 28(1):482–489

    Article  Google Scholar 

  • Kulkarni RV, Venayagamoorthy GK (2010) Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans Syst Man Cybern C 40(6):663–675

    Article  Google Scholar 

  • Li F, Upadhyaya BR (2011) Design of sensor placement for an integral pressurized water reactor using fault diagnostic observability and reliability criteria. Nucl Technol 173(1):17–25

    Google Scholar 

  • Li R, He D, Bechhoefe E (2012a) Gear fault location detection for split torque gearbox using AE sensors. IEEE Trans Syst Man Cy C 42(6):1308–1317

    Article  Google Scholar 

  • Li F, Upadhyaya BR, Perillo SRP (2012b) Fault diagnosis of helical coil steam generator systems of an integral pressurized water reactor using optimal sensor selection. IEEE Trans Nucl Sci 59(2):403–410

    Article  Google Scholar 

  • Liu W, Gao WC, Sun Y, Xu MJ (2008) Optimal sensor placement for spatial lattice structure based on genetic algorithms. J Sound Vib 317(1–2):175–189

    Article  Google Scholar 

  • Martello S, Pisinger D, Toth P (2000) New trends in exact algorithms for the 0–1 knapsack problem. Eur J Oper Res 123(2):325–332

    Article  MathSciNet  MATH  Google Scholar 

  • Martin WN, Ghoshal A, Sundaresan MJ, Lebby GL, Pratap PR, Schulz MJ (2005) An artificial neural receptor system for structural health monitoring. Struct Health Monit 4(3):229–245

    Article  Google Scholar 

  • Mini S, Udgata SK, Sabat SL (2014) Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens J 14(3):636–644

    Article  Google Scholar 

  • Mohanty AR, Kar C (2006) Fault detection in a multistage gearbox by demodulation of motor current waveform. IEEE Trans Ind Electron 53(4):1285–1297

    Article  Google Scholar 

  • Nimityongskul S, Kammer DC (2009) Frequency response based sensor placement for the mid-frequency range. Mech Syst Signal Process 23(4):1169–1179

    Article  Google Scholar 

  • Pan HX, Wei XY (2010) Optimal placement of sensor in gearbox fault diagnosis based on VPSO. In: Proceedings of 6th international conference on natural computation,, pp 3383–3387

  • Pandey MD, Sarkar A (2002) Comparison of a simple approximation for multinormal integration with an importance sampling-based simulation method. Probab Eng Mech 17(2):215–218

    Article  Google Scholar 

  • Pourali M, Mosleh A (2013) A functional sensor placement optimization method for power systems health monitoring. IEEE Trans Ind Appl 49(4):1711–1719

    Article  Google Scholar 

  • Radac M, Precup R, Petriu EM, Preitl S (2014) Iterative data-driven tuning of controllers for nonlinear systems with constraints. IEEE Trans Ind Electron 61(11):6360–6368

    Article  Google Scholar 

  • Raghuraj R, Bhushan M, Engaswamy R (1999) Locating sensors in complex chemical plants based on fault diagnostic observability criteria. AIChE J 45(2):310–322

    Article  Google Scholar 

  • Seraji H, Serrano N (2009) A multisensor decision fusion system for terrain safety assessment. IEEE Trans Robot 25(1):99–108

    Article  Google Scholar 

  • Tao D, Tang SJ, Liu L (2013) Constrained artificial fish-swarm based area coverage optimization algorithm for directional sensor networks. In: Proceedings of IEEE 10th international conference on mobile Ad-Hoc and sensor systems (MASS2013). pp 304–309

  • Urrego LR, Moreno EG, Anglada FM, Salvador AC, Cucarella EQ (2013) Hybrid analysis in the latent nestling method applied to fault diagnosis. IEEE Trans Autom Sci Eng 10(2):415–430

    Article  Google Scholar 

  • Venkatasubramanian V, Rengaswamy R, Kavuri SN (2003) A review of process fault detection and diagnosis part II: qualitative models and search strategies. Comput Chem Eng 27(3):313–326

    Article  Google Scholar 

  • Wolf CM, Hanson KM, Lorenz RD, Valenzuela MA (2013) Using the traction drive as the sensor to evaluate and track deterioration in electrified vehicle gearboxes. IEEE Trans Ind Appl 49(6):2610–2618

    Article  Google Scholar 

  • Worden K, Burrows AP (2001) Optimal sensor placement for fault detection. Eng Struct 23(8):885–901

    Article  Google Scholar 

  • Yang SM, Qiu J, Liu GJ, Yang P (2013) Sensor selection and optimization for aerospace system health management under uncertainty testing. Trans Jpn Soc Aeronaut Space 56(4):187–196

    Article  Google Scholar 

  • Zappala D, Tavner PJ, Crabtree CJ, Sheng S (2014) Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis. IET Renew Power Gener 8(4):380–389

    Article  Google Scholar 

  • Zhen ZY, Wang DB, Liu YY (2009) Improved shuffled frog leaping algorithm for continuous optimization problem. In IEEE congress on evolutionary computation (CEC2009), pp 2992–2995

  • Zhong BL, Huang R (2007) Introduction to machine fault diagnosis. China machine press, Beijing

    Google Scholar 

  • Zhou XW, Wang YY, Tian XX, Guo RQ (1997) A tabu search algorithm for quadratic 0–1 programming problem. Chin Q J Math 12(4):98–102

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 51075070 and 51175001, in part by the Jiangsu Province Research Innovation Program for College Graduates, China under Grant CXZZ_0139, in part by the Anhui Province Foundation for Youth Scholars of Educational Commission, China under Grant 2012SQRL085 and Anhui Province Natural Science Foundation of China under Grant 1308085ME78, and in part by the Macao Science and Technology Development Fund under Grant 052/2014/A1.

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Authors and Affiliations

  1. School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Zhuanzhe Zhao & Minping Jia

  2. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, 241000, China

    Zhuanzhe Zhao

  3. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Avenida da Universidade, Taipa, Macau, China

    Qingsong Xu

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  1. Zhuanzhe Zhao
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  3. Minping Jia
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Correspondence to Minping Jia.

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Zhao, Z., Xu, Q. & Jia, M. Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm. Nat Comput 15, 653–664 (2016). https://doi.org/10.1007/s11047-015-9515-4

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  • DOI: https://doi.org/10.1007/s11047-015-9515-4

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