[1] A K S Jardine, D M Lin, D Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006, 20(7): 1483-1510.
[2] Z W Gao, C Cecati, S X Ding. A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part I: Fault Diagnosis with ModelBased and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757-3767.
[3] R B Randall. Vibration-based condition monitoring: industrial, aerospace and automotive applications. Chichester: John Wiley & Sons, Ltd., 2011.
[4] J Z Sikorska, M Hodkiewicz, L Ma. Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 2011, 25(5): 1803-1836.
[5] A W Heng, S Zhang, A C C Tan, et al. Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 2009, 23(3): 724-739.
[6] Y Gui, Q K Han, F L Chu. A vibration model for fault diagnosis of planetary gearboxes with localized planet bearing defects. Journal of Mechanical Science and Technology, 2016, 30(9): 4109-4119.
[7] S Xue, I Howard. Dynamic modelling of flexibly supported gears using iterative convergence of tooth mesh stiffness. Mechanical Systems and Signal Processing, 2016, 80: 460-481.
[8] O D Mohammed, M Rantatalo. Dynamic response and timefrequency analysis for gear tooth crack detection. Mechanical Systems and Signal Processing, 2016, 66-67: 612-624.
[9] I El-Thalji, E Jantunen. Dynamic modelling of wear evolution in rolling bearings. Tribology International, 2015, 84: 90-99.
[10] I El-Thalji, E Jantunen. A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2015, 60-61: 252-272.
[11] M Eremia, C C Liu, A Edris. Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence. Hoboken: John Wiley & Sons, Inc., 2016.
[12] V Piuri, F Scotti, M Roveri. Computational Intelligence in Industrial Quality Control//Proceedings of the IEEE 2005 International Workshop on Intelligent Signal Processing, Faro, Portugal, September 1-3, 2005: 4-9.
[13] X Y Wang, Y M Ding. Adaptive Real-time Predictive Compensation Control for 6-DOF Serial Arc Welding Manipulator. Chinese Journal of Mechanical Engineering, 2010, 23(3): 361-366.
[14] Q K Al-Shayea. Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues, 2011, 8(2): 150-154.
[15] R D Labati, A Genovese, E Munñoz, et al. Computational intelligence for industrial and environmental applications//Proceedings of the IEEE 8th International Conference on Intelligent Systems, Sofia, Bulgaria, September 4-6, 2016: 8-14.
[16] P K Kankar, S C Sharma, S P Harsha. Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 2011, 38(3): 1876-1886.
[17] S Kar, S Das, P K Ghosh. Applications of neuro fuzzy systems: A brief review and future outline. Applied Soft Computing, 2014, 15: 243-259.
[18] J Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 2015, 61: 85-117.
[19] G B Huang, Q Y Zhu, C Siew. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1): 489-501.
[20] L Cuadra, S Salcedo-Sanz, J C Nieto-Borge, et al. Computational intelligence in wave energy: Comprehensive review and case study. Renewable and Sustainable Energy Reviews, 2016, 58: 1223-1246.
[21] R C Cavalcante, R C Brasileiro, V L F Souza, et al. Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 2016, 55: 194-211.
[22] O P Mahela, A G Shaik, N Gupta. A critical review of detection and classification of power quality events. Renewable and Sustainable Energy Reviews, 2015, 41: 495-505.
[23] S Khokhar, A A B Mohd Zin, A S B Mokhtar, et al. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 2015, 51: 1650-1663.
[24] P Henriquez, J B Alonso, M A Ferrer, et al. Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 44(5): 642-652.
[25] M S Kan, A C C Tan, J Mathew. A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing, 2015, 62-63: 1-20.
[26] J D Wu, C H Liu. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Systems with Applications, 2009, 36(3): 4278-4286.
[27] Y G Lei, Z J He, Y Y Zi. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Systems with Applications, 2011, 38(6): 7334-7341.
[28] G F Bin, J J Gao, X J Li, et al. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 2012, 27: 696-711.
[29] L L Cui, C Q Ma, F B Zhang, et al. Quantitative diagnosis of fault severity trend of rolling element bearings. Chinese Journal of Mechanical Engineering, 2015, 28(6): 1254-1260.
[30] N Saravanan, K I Ramachandran. Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 2010, 37(6): 4168-4181.
[31] Z Zhao, Q S Xu, M P Jia. Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis. Neural Computing and Applications, 2016, 27(2): 375-385.
[32] F Jia, Y G Lei, J Lin, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016, 72-73: 303-315.
[33] G E Hinton, R R Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 2006, 313(5786): 504-507.
[34] V T Tran, F AlThobiani, A Ball. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Systems with Applications, 2014, 41(9): 4113-4122.
[35] P Tamilselvan, P F Wang. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 2013, 115: 124-135.
[36] X J Guo, L Chen, C Q Shen. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93: 490-502.
[37] Z X Yang, X B Wang, J H Zhong. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach. Energies, 2016, 9(6):379
[38] Y Wei, X Minqiang, L Yongbo, et al. 1983. Gearbox fault diagnosis based on local mean decomposition, permutation entropy and extreme learning machine. Journal of Vibroengineering, 2016, 18(3): 1459-1473.
[39] P K Wong, Z Yang, C M Vong, et al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing, 2014, 128: 249-257.
[40] Y Tian, J Ma, C Lu, et al. Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. Mechanism and Machine Theory, 2015, 90: 175-186.
[41] M Mishra, M Sahani, P K Rout. An Islanding Detection Method Based on Wavelet Packet Transform and Extreme Learning Machine. International Journal of Renewable Energy Research (IJRER), 2016, 6(3): 856-865.
[42] S Li, G Wu, B Gao, et al. Interpretation of DGA for Transformer Fault Diagnosis with Complementary SaE-ELM and Arctangent Transform. IEEE Transactions on Dielectrics and Electrical Insulation, 2016, 23(1): 586-595.
[43] M A El-Gamal, M Abdulghafour. Fault isolation in analog circuits using a fuzzy inference system. Computers & Electrical Engineering, 2003, 29(1): 213-229.
[44] H Eristi. Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neurofuzzy inference system. Measurement, 2013, 46(1): 393-401.
[45] J Chen, C Roberts, P Weston. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Control Engineering Practice, 2008, 16(5): 585-596.
[46] S M El-Shal, 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: Applications and Reviews, 2000, 30(2): 281-289.
[47] J Zheng, J Cheng, Y Yang. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mechanism and Machine Theory, 2013, 70: 441-453.
[48] L Zhang, G Xiong, H Liu, et al. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Systems with Applications, 2010, 37(8): 6077-6085.
[49] V T Tran, B Yang, M Oh, et al. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 2009, 36(2): 1840-1849.
[50] J Wu, C Hsu, G Wu. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Systems with Applications, 2009, 36(3): 6244-6255.
[51] J M Li, Y G Zhang, P Xie. A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis. Measurement, 2016, 91: 499-508.
[52] N Lu, Z H Xiao, O P Malik. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2015, 52-53: 393-415.
[53] W S Su, F T Wang, H Zhu, et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement. Mechanical Systems and Signal Processing, 2010, 24(5): 1458-1472.
[54] X A Yan, M P Jia. Parameter optimized combination morphological filter-hat transform and its application in fault diagnosis of wind turbine. Joural of Mechanical Engineering, 2016, 52(13): 103-110.(in Chinese)
[55] W Zhang, M P Jia, L Zhu. An adaptive Morlet wavelet filter method and its application in detecting early fault feature of ball bearings. Joural of southeast university (Natural Science Edition), 2016, 46(3): 457-463.(in Chinese)
[56] X A Yan, M P Jia, L Xiang. Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum. Measurement Science and Technology, 2016, 27: 75002.
[57] C Ciancio, G Ambrogio, F Gagliardi, et al. Heuristic techniques to optimize neural network architecture in manufacturing applications. Neural Computing and Applications, 2016, 27(7): 2001-2015.
[58] M Unal, M Onat, M Demetgul, et al. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 2014, 58: 187-196.
[59] H Shao, H Jiang, X Zhang, et al. Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 2015, 26: 115002.
[60] M Demetgul, M Unal, I N Tansel, et al. Fault diagnosis on bottle filling plant using genetic-based neural network. Advances in Engineering Software, 2011, 42(12): 1051-1058.
[61] F Leung, H Lam, S Ling, et al. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks, 2003, 14(1): 79-88.
[62] F Chen, B Tang, T Song, et al. Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement, 2014, 47: 576-590.
[63] M Unal, M Onat, M Demetgul, et al. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 2014, 58: 187-196.
[64] F Chen, B Tang, R Chen. A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm. Measurement, 2013, 46(1): 220-232.
[65] H Sadegh, A N Mehdi, A Mehdi. Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm. Tribology International, 2016, 95: 426-434.
[66] A Saxena, A Saad. Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing, 2007, 7(1): 441-454.
[67] M Ahmed, F Gu, A Ball. Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network//2011 9th International Conference on Damage Assessment of Structures (DAMAS), London, United Kingdom, July 11-13, 2011: 12112.
[68] M Cerrada, R Sa'nchez, D Cabrera, et al. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal. Sensors, 2015, 15(9): 23903-23926.
[69] M Cerrada, G Zurita, D Cabrera, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mechanical Systems and Signal Processing, 2016, 70-71: 87-103.
[70] C Rajeswari, B Sathiyabhama, S Devendiran, et al. A Gear Fault Identification using Wavelet Transform, Rough set Based GA, ANN and C4.5 Algorithm. Procedia Engineering, 2014, 97: 1831-1841.
[71] L B Jack, A K Nandi. Support vector machines for detection and characterization of rolling element bearing faults. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2001, 215(9): 1065-1071.
[72] P Konar, P Chattopadhyay. Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 2011, 11(6): 4203-4211.
[73] N Li, R Zhou, Q Hu, et al. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine. Mechanical Systems and Signal Processing, 2012, 28: 608-621.
[74] J Cheng, D Yu, J Tang. Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery. Shock and Vibration, 2009, 16(1): 89-98.
[75] X Zhang, Y Liang, J Zhou, et al. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 2015, 69: 164-179.
[76] S Wang, A Mathew, Y Chen, et al. Empirical analysis of support vector machine ensemble classifiers. Expert Systems with Applications, 2009, 36(3): 6466-6476.
[77] J Tian, H Gu, W Liu. Imbalanced classification using support vector machine ensemble. Neural Computing and Applications, 2011, 20(2): 203-209.
[78] J Zheng, H Pan, J Cheng. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 2017, 85: 746-759.
[79] S Abe. Fuzzy support vector machines for multilabel classification. Pattern Recognition, 2015, 48(6): 2110-2117.
[80] J Hang, J Zhang, M Cheng. Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine. Fuzzy Sets and Systems, 2016, 297: 128-140.
[81] Z Yin, J Hou. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 2016, 174: 643-650.
[82] X Zhang, W Chen, B Wang, et al. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing, 2015, 167: 260-279.
[83] X Zhang, B Wang, X Chen. Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 2015, 89: 56-85.
[84] Y Li, M Xu, Y Wei, et al. A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree. Measurement, 2016, 77: 80-94.
[85] Z Shen, X Chen, X Zhang, et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement, 2012, 45(1): 30-40.
[86] R Liu, B Yang, X Zhang, et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mechanical Systems and Signal Processing, 2016, 75: 345-370.
[87] C Vong, P Wong, L Tam, et al. Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis using Wavelet Packet Transform and Support Vector Machines. Chinese Journal of Mechanical Engineering, 2011, 24(5): 870-878.
[88] M J Zhang, J Tang, X M Zhang, et al. Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples. Chinese Journal of Mechanical Engineering, 2016, 29(2): 396-405.
[89] C Hu, B D Youn, P F Wang, et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 2012, 103: 120-135.
[90] J Lee, F J Wu, W Y Zhao, et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 2014, 42(1-2): 314-334.
[91] A M Riad, H K Elminir, H M Elattar. Evaluation of Neural Networks in the Subject of Prognostics As Compared To Linear Regression Model. International Journal of Engineering & Technology, 2010, 10(6): 52-58.
[92] Z Y Zhang, Y Wang, K S Wang. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 2013, 24(6): 1213-1227.
[93] Z G Tian, L Wong, N Safaei. A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing, 2010, 24(5): 1542-1555.
[94] F Ahmadzadeh, J Lundberg. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 2013, 53: 1-8.
[95] J A Rodríguez, Y E Hamzaoui, J A Hernández, et al. The use of artificial neural network (ANN) for modeling the useful life of the failure assessment in blades of steam turbines. Engineering Failure Analysis, 2013, 35: 562-575.
[96] A Malhi, R Q Yan, R X Gao. Prognosis of Defect Propagation Based on Recurrent Neural Networks. IEEE Transactions on Instrumentation and Measurement, 2011, 60(3): 703-711.
[97] A K Mahamad, S Saon, T Hiyama. Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications, 2010, 60(4): 1078-1087.
[98] B Chen, P C Matthews, P J Tavner. Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems with Applications, 2013, 40(17): 6863-6876.
[99] S Hong, Z Zhou, E Zio, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing, 2014, 27: 159-166.
[100] K Javed, R Gouriveau, N Zerhouni. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering. IEEE Transactions on Cybernetics, 2015, 45(12): 2626-2639.
[101] C Zhang, P Lim, A K Qin, et al. Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics. IEEE Transactions on Neural Networks and Learning Systems, 2016: 1-13.
[102] C F Baban, M Baban, M D Suteu. Using a fuzzy logic approach for the predictive maintenance of textile machines. Journal of Intelligent & Fuzzy Systems, 2016, 30(2): 999-1006.
[103] R Stetter, M Witczak. Degradation Modelling for Health Monitoring Systems. Journal of Physics: Conference Series, 2014, 570(6): 62002.
[104] R Ishibashi, C L N Ju'nior. GFRBS-PHM: A Genetic Fuzzy Rule-Based System for PHM with Improved Interpretability//Proceedings of the IEEE Conference on Prognostics and Health Management (PHM), Milan, Italy, September 8-11, 2013: 1-7.
[105] X M Tian, Y P Cao, S Chen. Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor. International Journal of Adaptive Control and Signal Processing, 2011, 25(9): 813-830.
[106] E Zio, F Di Maio. A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety, 2010, 95(1): 49-57.
[107] C J Zhang, X F Yao, J M Zhang, et al. Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations. Sensors, 2016, 16(6): 795.
[108] J Gokulachandran, K Mohandas. Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools. Journal of Intelligent Manufacturing, 2015, 26(2): 255-268.
[109] J B Ali, B Chebel-Morello, L Saidi, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 2015, 56-57: 150-172.
[110] B D Chen, P C Matthews, P J Tavner. Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems with Applications, 2013, 40(17): 6863-6876.
[111] F G ZHAO, J CHEN, L GUO, et al. Neuro-fuzzy Based Condition Prediction of Bearing Health. Journal of Vibration and Control, 2009, 15(7): 1079-1091.
[112] C C Chen, B Zhang, G Vachtsevanos. Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms. IEEE Transactions on Instrumentation and Measurement, 2012, 61(2): 297-306.
[113] E Ramasso, R Gouriveau. Remaining Useful Life Estimation by Classification of Predictions Based on a Neuro-Fuzzy System and Theory of Belief Functions. IEEE Transactions on Reliability, 2014, 63(2): 555-566.
[114] W Wang, D Z Li, J Vrbanek. An evolving neuro-fuzzy technique for system state forecasting. Neurocomputing, 2012, 87: 111-119.
[115] T Boukra. Identifying New Prognostic Features for Remaining Useful Life Prediction Using Particle Filtering and Neuro-Fuzzy System Predictor//Proceedings of the IEEE 2015 15th International Conference on Environment and Electrical Engineering, Rome, Italy, June 10-13, 2015: 1533-1538.
[116] Z L Du, X M Li, Q Mao. A new online hybrid learning algorithm of adaptive neural fuzzy inference system for fault prediction. International Journal of Modeling Identification and Control, 2015, 23(1): 68-76.
[117] C C Chen, B Zhang, G Vachtsevanos, et al. Machine Condition Prediction Based on Adaptive Neuro -Fuzzy and High-Order Particle Filtering. IEEE Transactions on Industrial Electronics, 2011, 58(9): 4353-4364.
[118] C C Chen, G Vachtsevanos, M E Orchard. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach. Mechanical Systems and Signal Processing, 2012, 28: 597-607.
[119] C C Chen, G Vachtsevanos. Bearing condition prediction considering uncertainty: An interval type-2 fuzzy neural network approach. Robotics and Computer-Integrated Manufacturing, 2012, 28(4): 509-516.
[120] G Bosque, I Del Campo, J Echanobe. Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades. Engineering Applications of Artificial Intelligence, 2014, 32: 283-331.
[121] Y Pan, M J Er, X Li, et al. Machine health condition prediction via online dynamic fuzzy neural networks. Engineering Applications of Artificial Intelligence, 2014, 35: 105-113.
[122] L Zhang, G L Xiong, L P Liu, et al. Gearbox health condition identification by neuro-fuzzy ensemble. Journal of Mechanical Science and Technology, 2013, 27(3): 603-608.
[123] V Vapnik. The nature of statistical learning theory. New York: Springer, 1995.
[124] C Lu, J Chen, R J Hong, et al. Degradation trend estimation of slewing bearing based on LSSVM model. Mechanical Systems and Signal Processing, 2016, 76-77: 353-366.
[125] S J Dong, T H Luo. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement, 2013, 46(9): 3143-3152.
[126] X F Chen, Z J Shen, Z J He, et al. Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227(12): 2849-2860.
[127] W Caesarendra, A Widodo, B Yang. Combination of probability approach and support vector machine towards machine health prognostics. Probabilistic Engineering Mechanics, 2011, 26(2): 165-173.
[128] A Widodo, B Yang. Machine health prognostics using survival probability and support vector machine. Expert Systems with Applications, 2011, 38(7): 8430-8437.
[129] V T Tran, H Thom Pham, B Yang, et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing, 2012, 32: 320-330.
[130] T H Loutas, D Roulias, G Georgoulas. Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression. IEEE Transactions on Reliability, 2013, 62(4): 821-832.
[131] K He, Q S Xu, M P Jia. Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model. IEEE Transactions on Automation Science and Engineering, 2015, 12(3): 1092-1103.
[132] H Z Huang, H K Wang, Y F Li, et al. Support vector machine based estimation of remaining useful life: current research status and future trends. Journal of Mechanical Science and Technology, 2015, 29(1): 151-163.
[133] Z L Liu, M J Zuo, Y Qin. Remaining useful life prediction of rolling element bearings based on health state assessment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016, 230(2): 314-330.
[134] Q Zhang, F Liu, X Wan, et al. An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems. Shock and Vibration, 2015, 2015: 1-8.
[135] Y N Pan, J Chen, L Guo. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description. Mechanical Systems and Signal Processing, 2009, 23(3): 669-681.
[136] T Benkedjouh, K Medjaher, N Zerhouni, et al. Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 2013, 26(7): 1751-1760.
[137] H Kim, A C C Tan, J Mathew, et al. Bearing fault prognosis based on health state probability estimation. Expert Systems with Applications, 2012, 39(5): 5200-5213.
[138] T H Yi, H N Li. Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures. International Journal of Distributed Sensor Networks, 2012, 2012: 1-11.
[139] A R M Rao, G Anandakumar. Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique. Smart Materials and Structures, 2007, 16: 2658-2672.
[140] D S Li, H N Li, C Fritzen. Load dependent sensor placement method: Theory and experimental validation. Mechanical Systems and Signal Processing, 2012, 31: 217-227.
[141] H Y Guo, L Zhang, L L Zhang, et al. Optimal placement of sensor for structural health monitoring using improved genetic algorithms. Smart Materials and Structures, 2004, 13: 528-534.
[142] S H Mahdavi, H A Razak. Optimal sensor placement for timedomain identification using a wavelet-based genetic algorithm. Smart Materials and Structures, 2016, 25: 65006.
[143] R Dutta, R Ganguli, V Mani. Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains. Smart Materials and Structures, 2011, 20: 105018.
[144] K Worden, A P Burrows. Optimal sensor placement for fault detection. Engineering Structures, 2001(23): 885-901.
[145] H Pan, X Wei. Optimal Placement of Sensor in Gearbox Fault Diagnosis Based on VPSO//Proceedings of the IEEE 2010 6th International Conference on Natural Computation, Yantai, China, August 10-12, 2010: 3383-3387.
[146] Y Ren, Y Ding. Optimal sensor distribution in multi-station assembly processes for maximal variance detection capability. ⅡE Transactions, 2009, 41: 804-818.
[147] N Shukla, D Ceglarek, M K Tiwari. Key characteristics-based sensor distribution in multi-station assembly processes. Journal of Intelligent Manufacturing, 2015, 26(1): 43-58.
[148] M M Abdullah, A Richardson, J Hanif. Placement of sensors/actuators on civil structures using genetic algorithms. Earthquake Engineering & Structural Dynamics, 2001, 30(8): 1167-1184.
[149] P Tongpadungrod, T D L Rhys, P N Brett. An approach to optimise the critical sensor locations in one-dimensional novel distributive tactile surface to maximise performance. Sensors and Actuators A: Physical, 2003, 105(1): 47-54.
[150] J J Lian, L J He, B Ma, et al. Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm. Smart Materials and Structures, 2013, 22: 95015.
[151] H M Chow, H F Lam, T Yin, et al. Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating. Structural Control and Health Monitoring, 2011, 18(3): 305-320.
[152] Z Zhao, Q S Xu, M P Jia. Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm. Natural Computing, 2015: 1-12.
[153] K He, M P Jia, Z Z Zhao. Sensor Optimization for Cutting Status Monitoring in Single Manufacturing Unit. Advanced Materials Research, 2012, 569: 636-639.
[154] K He, M P Jia, Q S Xu. Optimal Sensor Deployment for Manufacturing Process Monitoring Based on Quantitative Cause-Effect Graph. IEEE Transactions on Automation Science and Engineering, 2016, 13(2): 963-975.
