Ensuring Vibration Reliability of Turbopump Units Using Artificial Neural Networks

This paper is devoted to developing the scientific approach of using artificial neural networks for solving a significant problem of vibration reliability of rotary machines that is urgently needed to improve the quality of their diagnosis and manufacturing. The proposed methodology integrates analytical dependencies, recent techniques of numerical simulations and artificial neural networks. The design schemes for realizing the related approach are presented on the example of the turbopump unit for liquid rocket engine. The main advantage of this approach in comparison with the traditional regression analysis and other existing techniques is absence of necessity for setting trial imbalances and carrying out additional initial starts of the turbopump unit. The mathematical model for identification nonlinear parameters of the dependence between bearing stiffness, deflection of the rotary axis, and rotor speed is presented. The proposed methodology is proved by the research of rotor dynamics on the example of turbopump units for liquid rocket engines and allows refining parameters of the nonlinear mathematical models describing forced oscillations of the rotor as a complicated mechanical system with nonlinearities. The results of the research can be used for carrying out the virtual balancing procedure for identification the system of imbalances by the reliable model of forced oscillation of the system “rotor – bearing supports”.

[1]  Weiji Wang,et al.  CLASSIFICATION OF WAVELET MAP PATTERNS USING MULTI-LAYER NEURAL NETWORKS FOR GEAR FAULT DETECTION , 2002 .

[2]  Vitalii Oleksandrovych Ivanov,et al.  Application of Artificial Neural Network for Identification of Bearing Stiffness Characteristics in Rotor Dynamics Analysis , 2018, Lecture Notes in Mechanical Engineering.

[3]  Ivan Volodymyrovych Pavlenko,et al.  Dynamic analysis of centrifugal machines rotors supported on ball bearings by combined application of 3D and beam finite element models , 2017 .

[4]  Alexander Gusak,et al.  Application of Small-Sized Low Speed Axial Stages in Well Pumps for Water Supply , 2012 .

[5]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[6]  Jan Pital,et al.  Computational intelligence and low cost sensors in biomass combustion process , 2013, 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA).

[7]  Robert B. Randall,et al.  Differential Diagnosis of Gear and Bearing Faults , 2002 .

[8]  Gaoliang Peng,et al.  Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input , 2017 .

[9]  Vitalii Oleksandrovych Ivanov,et al.  Estimation of the Reliability of Automatic Axial-balancing Devices for Multistage Centrifugal Pumps , 2018, Periodica Polytechnica Mechanical Engineering.

[10]  Vitalii Oleksandrovych Ivanov,et al.  Scientific and Methodological Approach for the Identification of Mathematical Models of Mechanical Systems by Using Artificial Neural Networks , 2018, Innovation, Engineering and Entrepreneurship.

[11]  Ming Dong,et al.  Equipment health diagnosis and prognosis using hidden semi-Markov models , 2006 .

[12]  Adam Hamrol,et al.  Artificial Neural Networks as a Means for Making Process Control Charts User Friendly , 2017 .

[13]  Maria Leonilde Rocha Varela,et al.  A Methodology of Improvement of Manufacturing Productivity Through Increasing Operational Efficiency of the Production Process , 2018 .

[14]  Ivan Volodymyrovych Pavlenko,et al.  Static and Dynamic Analysis of the Closing Rotor Balancing Device of the Multistage Centrifugal Pump , 2014 .

[15]  Khalid F. Al-Raheem,et al.  Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis , 2011 .

[16]  Chun-Chieh Wang,et al.  Applications of fault diagnosis in rotating machinery by using time series analysis with neural network , 2010, Expert Syst. Appl..

[17]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[18]  Peng Wang,et al.  Fault prognostics using dynamic wavelet neural networks , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[19]  A S Yashchenko,et al.  Effect of Bearing Housings on Centrifugal Pump Rotor Dynamics , 2017 .

[20]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[21]  Zhigang Tian,et al.  An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring , 2012, J. Intell. Manuf..

[22]  O. Obukhov,et al.  Numerical and experimental investigation of the efficiency of vaned diffuser of centrifugal compressor , 2013 .

[23]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[24]  A. K. Wadhwani,et al.  Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis , 2011 .

[25]  Vasyl Martsynkovskyy,et al.  Comparative Tribological Tests for Face Impulse Seals Sliding Surfaces Formed by Various Methods , 2018, Lecture Notes in Mechanical Engineering.

[26]  A. S. Sekhar,et al.  Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems , 2013 .