Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

Neurocomputing

Volumn 238, Issue , 2017, Pages 13-23

  Gao, Zehai ^a   Ma, Cunbao ^a   Song, Dong ^a   Liu, Yang ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Aircraft fuel system; Deep belief network; Failure mode; Fault diagnosis; Quantum inspired neural network

Indexed keywords

AIRCRAFT; AIRCRAFT FUELING; AIRCRAFT FUELS; DEEP NEURAL NETWORKS; FAILURE ANALYSIS; FAILURE MODES; FAULT DETECTION; FUEL SYSTEMS;

COMPLICATED SYSTEMS; CORE COMPONENTS; DEEP BELIEF NETWORK (DBN); DEEP BELIEF NETWORKS; HIDDEN LAYERS; LINEAR SUPERPOSITIONS; QUANTUM INSPIRED NEURAL NETWORKS; QUANTUM NETWORK;

NEURAL NETWORKS;

AIRCRAFT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSICAL DEEP BELIEF NETWORK; CONTROLLED STUDY; DEEP QUANTUM INSPIRED NEURAL NETWORK; FLIGHT; MACHINE LEARNING; MICROELECTROMECHANICAL SYSTEM; QUANTUM YIELD;

EID: 85011575282     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2017.01.032     Document Type: Article

References (47)

1. [1] Jimenez, J.F., Giron-Sierra, J.M., Insaurralde, C., A simulation of aircraft fuel management system. Simul. Model. Pract. Theory 15 (2007), 544–564.
2. [2] Shen, T., Wan, F.Y., Cui, W.M., Song, B.F., Application of prognostic and health management technology on aircraft fuel system. Proceedings of the Prognostics and Health Management Conference, China, 2010, 1–7.
3. [3] Papadopoulos, Y., Model-based system monitoring and diagnosis of failures using Statecharts and fault trees. Reliab. Eng. Syst. Saf. 81 (2003), 325–341.
4. [4] Bartlett, L.M., Hurdle, E.E., Kelly, E.M., Integrated system fault diagnostics utilising digraph and fault tree-based approaches. Reliab. Eng. Syst. Saf. 94 (2009), 1107–1115.
5. [5] Abdelwahed, S., Karsai, G., Mahadevan, N., Ofsthun, S.C., Practical implementation of diagnosis systems using timed failure propagation graph models. IEEE Trans. Instrum. Meas. 58 (2008), 240–247.
6. [6] Narasimhan, S., Biswas, G., Model-based diagnosis of hybrid systems. IEEE Trans. Syst. Man. Cybern. 37 (2007), 348–361.
7. [7] Kak, S., On quantum neural computing. Inf. Sci. 83 (1995), 143–160.
8. [8] Matsui, N., Takai, M., Nishimura, H., A network model based on qubit like neuron corresponding to quantum circuit. Electron. Commun. Jpn. 83:10 (2000), 67–73.
9. [9] Li, P., Li, S., Learning algorithm and application of quantum bp neural networks based on universal quantum gates. J. Syst. Eng. Electron. 19:1 (2008), 167–174.
10. [10] Liu, C., Chen, C., Chang, C., Shih, L., Single-hidden-layer feed-forward quantum neural network based on grover learning. Neural Netw.(45), 2013, 144–150.
11. [11] Gandhi, V., Prasad, G., Coyle, D., Behera, L., McGinnity, T., Evaluating quantum neural network filtered motor imagery brain-computer interface using multiple classification techniques. Neurocomputing 170 (2015), 161–167.
12. [12] Schmidhuber, J., Deep learning in neural networks: an overview. Neural Netw. 61 (2015), 85–117.
13. [13] Salakhutdinov, R., Hinton, G., An efficient learning procedure for deep Boltzmann machines. Neural Comput. 24:8 (2012), 1967–2006.
14. [14] Hinton, G.E., Osindero, S., Teh, Y., A fast learning algorithm for deep belief nets. Neural Comput. 18 (2006), 1527–1554.
15. [15] Tamilselvan, P., Wang, P., Failure diagnosis using deep belief learning based health state classification. Reliab. Eng. Syst. Saf. 115 (2013), 124–135.
16. [16] Hinton, G., Salakhutdinov, R., Reducing the dimensionality of data with neural networks. Science 313 (2006), 504–507.
17. [17] Roux, N., Bengio, Y., Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput. 20:6 (2008), 1631–1649.
18. [18] Ackley, D., Hinton, G., Sejnowski, T., A learning algorithm for Boltzmann machines. Cogn. Sci. 9 (1985), 147–169.
19. [19] Li, H., Li, X., Ramanathan, M., Zhang, A., Identifying informative risk factors and predicting bone disease progression via deep belief networks. Methods 69 (2014), 257–265.
20. [20] Huang, W., Song, G., Hong, H., Xie, K., Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15 (2014), 2191–2201.
21. [21] Wei, Y., Qiu, J., Karimi, H., Quantized H infinity filtering for continuous-time markovian jump systems with deficient mode information. Asian J Control. 17 (2015), 1914–1923.
22. [22] Wei, Y., Qiu, J., Karimi, H., Wang, M., Model approximation for two-dimensional markovian jump systems with state-delays and imperfect mode information. Multid. Syst. Sign Process. 26 (2015), 575–597.
23. [23] Wei, Y., Qiu, J., Karimi, H., Wang, M., New results on H infinity dynamic output feedback control for markovian jump systems with time-varying delay and defective mode information. Optim. Control appl. Met. 35 (2014), 656–675.
24. [24] Abrams, D., Lloyd, S., Nonlinear quantum mechanics implies polynomial-time solution for np-complete and #p problems. Phys. Rev. Lett. 18 (1998), 3992–3995.
25. [25] da Silva, A., Ludermir, T., de Oliveira, W., Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Netw. 76 (2016), 55–64.
26. [26] da Silva, A., de Oliveira, W., Ludermir, T., Classical and superposed learning for quantum weightless neural networks. Neurocomputing 75 (2012), 52–60.
27. [27] Matsui, N., Kouda, N., Nishimura, H., Neural network based on QBP and its performance. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Italy, 2000, 247–252.
28. [28] da Silva, A., de Oliveira, W., Ludermir, T., Weightless neural network parameters and architecture selection in a quantum computer. Neurocomputing 183 (2016), 13–22.
29. [29] Li, P., Xiao, H., Shang, F., Tong, X., X. Li, M.C., A hybrid quantum-inspired neural networks with sequence inputs. Neurocomputing 117 (2013), 81–90.
30. [30] Kouda, N., Matsui, N., Nishimura, H., Peper, F., Qubit neural network and its learning efficiency. Neural Comput. Appl. 14:2 (2013), 114–121.
31. [31] Righi, Z., Nan, J., Qiulin, D., Model and training of qnn with weight. Neural Process. Lett. 24:3 (2006), 261–269.
32. [32] Purushothaman, G., Karayiannis, N., Quantum neural networks QNN's: inherently fuzzy feedforward neural networks. IEEE Trans. Neural Netw. 8:3 (1997), 679–693.
33. [33] Karimi, H., Gao, H., New delay-dependent exponential h-infinity synchronization for uncertain neural networks with mixed time delays. IEEE Trans. Syst. Man Cyber. 40 (2011), 173–185.
34. [34] Zhou, S., Chen, Q., Wang, X., Deep quantum networks for classification. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), USA, 2014, 2885–2888.
35. [35] Ding, L., Chen, L., Research on quantum neural networks and its convergence property. Proceedings of the Fourth International Conference on Natural Computation, China, 3, 2008, 296–300.
36. [36] Lee, H., Chen, C., Huang, T., Learning efficiency improvement of back-propagation algorithm by error saturation prevention method. Neurocomputing 41 (2002), 125–143.
37. [37] Bathaie, S., Vanini, Z., Khorasani, K., Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 125 (2014), 153–165.
38. [38] Karimi, H., Chadl, M., Shi, P., Fault detection, isolation, and tolerant control of vehicles using soft computing methods. IET Control Theory A. 8 (2014), 655–657.
39. [39] Clemente, J., Mansour, W., Ayoubi, R., Serrano, F., Mecha, H., Ziade, H., Falou, W., Velazco, R., Hardware implementation of a fault-tolerant hopfield neural network on fpgas. Neurocomputing 171 (2016), 1606–1609.
40. [40] Blake, C., Merz, C., UCI Repository of Machine Learning Databases. 1998, Department of Information and Computer Sciences, University of California, Irvine, USA http://www.ics.uci.edu/~mlearn/MLRepository.html.
41. [41] Biswas, G., Mahadevan, S., A hierarchical model-based approach to systems health management. Proceedings of the IEEE Aerospace Conference, America, 2007, 1–14.
42. [42] Choi, J., Model based diagnostic of motor and pumps, Doctoral Dissertation. 12, 2006, Texas University, 32–35.
43. [43] Wong, P.K., Zhong, J.H., Yang, Z.X., Vong, C.M., Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis. Neurocomputing 174 (2016), 331–343.
44. [44] Qu, J., Zhang, Z.S., Gong, T., A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion. Neurocomputing 171 (2016), 837–853.
45. [45] Schuld, M., Sinayskiy, I., Petruccione, F., The quest for a quantum neural network. Quantum Inf. Process. 13 (2014), 2567–2586.
46. [46] Lian, J., Wang, J., Passivity of switched recurrent neural networks with time-varying delays. IEEE Trans. Neural Netw. 26 (2015), 357–366.
47. [47] Wang, D., Wang, Z., Li, G., Wang, W., Fault detection for switched positive systems under successive packet dropouts with application to the leslie matrix model. Int. J. Robust Nonlin. 26 (2016), 2807–2823.