A review of methods for excitation force reconstruction
1
AGH University of Science and Technology
Submission date: 2019-03-15
Final revision date: 2019-06-25
Acceptance date: 2019-06-25
Online publication date: 2019-07-01
Publication date: 2019-07-01
Diagnostyka 2019;20(3):11-19
KEYWORDS
TOPICS
ABSTRACT
In many mechanical systems research scenarios, including in particular control and monitoring of mechanical devices, reconstruction of external loads acting on a system is a matter of great importance. The paper provides taxonomy and review of available dynamic load identification methods in cases when a direct load applied to the structure is required to be reconstructed. Methods based on frequency and time domains as well as those relying on statistical and soft computing approaches are described. The authors present methods in possibly broad manner, including initial assumptions, workflow and some of the equations. For that reason, reader is able to initially assess methods' potential in particular applications and choose the method which best fit his needs.
REFERENCES (70)
1.
Balageas D, Fritzen CP, Guemes A. Structural Health Monitoring. ISTE Ltd, London, UK, 2006.
2.
Sanchez J, Benaroya H. Review of force reconstruction techniques. Journal of Sound and Vibration. 2014; 333(14):2999-3018. https://doi.org/10.1016/j.jsv.....
3.
Radkowski S, Zawisza M. Failure oriented diagnostic models in condition monitoring. Diagnostyka. 2009; 4(52):93-97.
4.
Uhl T. The inverse identification problem and its technical application. Archive of Applied Mechanics. 2007; 77(5):325-337.
5.
Omidi E, Mahmoodi NS, Shepard SW. Multi positive feedback control method for active vibration suppression in exible structures. Mechatronics. 2019; 33:23-33. https://doi.org/10.1111/str.12....
6.
Donoughue PO, Robin O, Berry A. Time-resolved identification of mechanical loadings on plates using the virtual fields method and defectometry measurements. Strain. 2018; 1-14, 2018. https://doi.org/10.1111/str.12....
7.
Mostafavi M, Collins DM, Peel MJ, Reinhard C, Barhli SM, Mills R, Marshall MB, Dwyer-Joyce RS, Connolley T. Dynamic contact strain measurement by time resolved stroboscopic energy dispersive synchrotron X ray diffraction. Strain. 2017; 53:1-13, 2017. https://doi.org/10.1111/str.12....
8.
Grediac M, Sur F, Blaysat B. The Grid Method for In-plane Displacement and Strain Measurement: A Review and Analysis. Strain. 2016; 52:205-243. https://doi.org/10.1111/str.12....
9.
Otsuka T, Okada T, Ikeno T, Shiomi K, Okuma M. Force identification of an outboard engine by experimental means of linear structural modeling and equivalent force transformation. Journal of Sound and Vibration. 2017; 308:541-547. https://doi.org/10.1016/j.jsv.....
10.
Parloo E, Verboven P, Guillaume P, Van Overmeire M. Force identification by means of in-operation modal models. Journal of Sound and Vibration. 2003; 262:161-173.
11.
Cardi AA, Adams DE, Walsh S. Ceramic Body Armor Single Impact Force Identification on a Compliant Torso Using Acceleration Response Mapping. Structural Health Monitoring/ 2016; 5(4). https://doi.org/10.1177/147592....
12.
Pons J, Jezequel L, Laborde J. Coherence analysis of transfer function and polynomial chaos for the measurement of vibratory forces by inverse method. In Proceeding of ISMA2016 including USD2016, 2016.
13.
Dobson B, Rider E. A review of the indirect calculation of excitation forces from measured structural response data. Proceedings of the Institution of Mechanical Engineering Part C Journal of Mechanical Engineering Science. 1990; 204:69-75.
14.
Rust A, Edlinger I. Active path tracking for vehicle noise source identification. Sound And Vibration. 2002; 36:14.
15.
Wyckaert K, Van der Auweraer H. Operational Analysis, Transfer Paths Analysis, Modal Analysis, Tools to Understand Road Noise Problems in Cars. In Proceedings of SAE Noise and Vibration Conference. 1995; 139-143.
16.
Zhang Q, Allemang RJ, Brown DL. Modal filter: Concept and applications. In Proceedings of 8th IMAC, 1990; 487-496.
17.
Mendrok K. Force identification with use of spatial filter based on ODS. Diagnostyka. 2015; 16(1):23-28.
18.
Mendrok K, Bochenska M, Dworakowski Z. Effectiveness evaluation of the damage localization with a local spatial filtration under variable external conditions. Diagnostyka. 2019; 20(1):55-61. https://doi.org/10.29354/diag/....
19.
Shih CY, Zhang Q, Allemang RJ. Force identification by using principle and modal coordinate transformation method. Vibration Analysis Techniques and Applications, ASME publication DE. 1989; 18(4):303-309.
20.
Li J. Application of mutual energy theorem for determining unknown force sources. In Proceedings of Internoise 88, Avignon, France. 1988.
21.
Nelson PA, Curtis ARD, Elliott SJ, Bullmore AJ. The minimum power output of free field point sources and the active control of sound. Journal of Sound and Vibration. 1987; 116(3):397-414.
22.
Giergiel J, Uhl T. Identification of the input impact type forces in mechanical systems. The Archives of Transport. 1989; 1(1):29-52.
23.
Giergiel J, Uhl T. Identification of the impact force in mechanical system. Archives of Machine Design. 1989; XXXVI(2-3).
24.
Pieczara J. Application of genetic algorithms for identification of load vector. Machine Dynamics Problems. 2005; 29(2):115-128.
25.
Busby HR, Trujillo DM. Optimal Regularization of an inverse dynamic problem. Computers and Structures. 1997; 63(2):243-248.
26.
Jankowski L. On-line identification of dynamic loads. Structural and Multidisciplinary Optimization. 2009; 37(6):609-623.
27.
Holnicki-Szulc J, Gierliski J. Structural Analysis, Design and Control by the Virtual Distortion Method. Wiley, Chichester. 1995.
28.
Zhu T, Xiao S, Yang G. Force identification in time domain based on dynamic programming. Applied Mathematics and Computation. 2014; 235:226-234. https://doi.org/10.1016/j.amc.....
29.
El-Bakari A, Teidj S, Khamlichi A, Jacquelin E. Predictability of Impact Force Localization by Using the Optimization Technique. Procedia Technology. 2016; 22:94-100. https://doi.org/10.1016/j.prot....
30.
Qiao B, Zhang X, Gao J, Liu R, Chen X. Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction. Mechanical Systems and Signal Processing. 2016; 85:93-115. https://doi.org/10.1016/j.ymss....
31.
Qiao B, Zhang X, Gao J, Chen X. Impact-force sparse reconstruction from highly incomplete and inaccurate measurements. Journal of Sound and Vibration. 2016; 376:72-94. https://doi.org/10.1016/j.jsv.....
32.
Carne TG, Randy ML, Bateman VI. Force reconstruction using the sum of weighted accelerations technique - max-at procedure. In Proceedings of the 12th International Modal Analysis Conference, 1994; 2251:1054 - 1062.
33.
Maes K, De Roeck G, Iliopoulos A, Weijtjens W, Devriendt C, Lombaert G. Kalman filter based strain estimation for fatigue assessment of an offshore monopile wind turbine. In Proceeding of ISMA2016 including USD2016. 2016.
34.
Tamarozzi T, Risaliti E, Rottiers W, Janssens K, Desmet W. Noise, ill-conditioning and sensor placement analysis for force estimation through virtual sensing. In Proceeding of ISMA2016 including USD2016. 2016.
35.
Yan G, Sun H, Buyukozturk O. Impact load identification for composite structures using Bayesian regularization and unscented Kalman filter. 2016. https://doi.org/10.1016/j.jsv.....
36.
Zhang CD, Xu YL. Structural damage identification via response reconstruction under unknown excitation. Structural Health Monitoring. 2016; 24(8):1-11. https://doi.org/10.1002/stc.19....
37.
Klinkov M, Fritzen CP. Online estimation of external loads from dynamic measurements. In Proceedings of ISMA. 2006; 3957-3968.
38.
Rubio-Hervas J, Reyhanoglu M, MacKunis W. Observer-based Sliding Mode Control of Rijketype Combustion Instability. Low Frequency Noise, Vibration and Active Control. 2015; 34(2):201-218. https://doi.org/10.1260/0263-0....
39.
Ha QP, Trinh H. State and input simultaneous estimation for a class of nonlinear systems. Automatica. 2004; 40:1779-1785.
40.
Czop P, Uhl T. Load identification methods based on parametric models for mechanical structures. In Proceedings of the 8th IEEE International Conference on Methods and Models in Automation and Robotics MMAR. 2002; 1015-1020.
41.
Mendrok K, Kurowski P, Uhl T. Operational forces identification from helicopter model in-flight data with the use of inverted regressive parametric models. In Proceedings of the ASME 2008 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, New York, USA. 2008.
42.
Larminat P, Thomas Y. Automatics of Linear Systems, vol 2 (in French). Flammarion Sciences, Paris, 1977.
43.
Ginsberg D, Fritzen CP. Impact identification and localization using a sample-force-dictionary General Theory and its applications to beam structures. Structural Monitoring and Maintenance. 2016; 3(3):195-214. https://doi.org/10.12989/smm.2....
44.
Rezayat A, Nassiri V, De Pauw B, Ertveldt J, Vanlanduit S, Guillaume P. Identification of dynamic forces using group-sparsity in frequency domain. Mechanical Systems and Signal Processing. 2016; 70-71:756-768. https://doi.org/10.1016/j.ymss....
45.
Haas DJ, Imber R. Identification of helicopter component loads using multiple regression. Journal of Aircraft. 1994; 31(4):929-935.
46.
Zion L. Predicting fatigue loads using regression diagnostics. In Proceedings of The American Helicopter Society Annual Forum. 1994; 1337-1358.
47.
Draper NR, Smith H. Applied Regression Analysis. Wiley-Interscience, Warsaw, Poland, 3rd edition. 1998.
48.
Stelzner DA, Kammer DC. Input force estimation using an inverse structural filter. In Proceedings of 17th IMAC, Orlando, Florida, USA. 1999; 954-960.
49.
Staszewski WJ, Worden K, Wardle R, Tomlinson GR. Fail-safe sensor distributions for impact detection in composite. Smart Materials and Structures. 2000; 9:298-303.
50.
Ghajari M, Sharif-Khodaei Z, Aliabadi MH, Apicella A. Identification of impact force for smart composite stiffened panels. Smart Materials and Structures. 2013; 22:13. https://doi.org/10.1088/0964-1....
51.
Worden K, Staszewski WJ, Hensman JJ. Natural computing for mechanical systems research: A tutorial overview. Mechanical Systems and Signal Processing. 2011; 25(1):4-111. https://doi.org/10.1016/j.ymss....
52.
Skliros C, Miguez ME, Fakhre A, Jennions J. A review of model based and data driven methods targeting hardware systems diagnostics. Diagnostyka. 2018; 20(1):3-21. https://doi.org/10.29354/diag/....
53.
Hossain MS, Chao OZ, Ismail Z, Noroozi S, Yee KS, Artificial Neural Networks for Vibration Based Inverse Parametric Identifications: A Review. Applied Soft Computing Journal. 2016; 52:203-219. https://doi.org/10.1016/j.asoc....
54.
Norgaard M, Ravn O, Poulsen NK, Hansen LK. Neural network for modeling and control of dynamic systems. Springer- Verlag, London, UK, 2000.
55.
Haykin S. Neural Networks A Comprehensive Foundation. Pearson Prentice Hall, 2001.
56.
McCool KM, Flitter LA, Haas DJ. Development and flight test evaluation of a rotor system load monitoring technology. Journal of American Helicopter Society. 2000; 45:19-27.
57.
Haas DJ, Milano J, Flitter L. Prediction of Helicopter Component Loads Using Neural Networks. Journal of American Helicopter Society. 1995; 40:72-82.
58.
Goral G, Bydon S, Uhl T. Intelligent transducers of in-operational loads in construction fatigue monitoring. Machine Dynamics Problems. 2002; 26(2/3):73-88.
59.
Fu Z, Cheng W. Force Identification by Using Support Vector Machine and Differential Evolution Optimization. Adv. in Neural Network Research & Appli, LNEE 67. 2010; 793-801.
60.
Fu Z, Cheng W, Yang Y. Force Identification by Using SVM and CPSO Technique. In ICSI 2010, Part II, LNCS 6146. 2010; 140-148.
61.
Worden K, Staszewski WJ. Impact Location and Quantification on a Composite Panel using Neural Networks and a Genetic Algorithm. Strain. 2000; 36(2):61-68.
62.
LeClerc JR, Worden K, Staszewski WJ, Haywood J. Impact detection in an aircraft composite panel - A neural-network approach. Journal of Sound and Vibration. 2007; 299(3):672-682.
63.
Lee MH, Chen TC. Intelligent fuzzy weighted input estimation method for the forces generated by an operating rotating machine. Measurement: Journal of the International Measurement Confederation. 2011; 44(5):917- 926. https://doi.org/10.1016/j.meas....
64.
Jiang X, Mahadevan S, Yuan Y. Fuzzy stochastic neural network model for structural system identification. Mechanical Systems and Signal Processing. 2016; 82:1-18. https://doi.org/10.1016/j.ymss....
65.
Mozaffari A, Behzadipour S, Kohani M. Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor. Applied Soft Computing Journal. 2014; 14(PART A):12-30. https://doi.org/10.1016/j.asoc....
66.
Golob M, Tovornik B. Input-output modelling with decomposed neuro-fuzzy ARX model. Neurocomputing. 2008;71(4-6):875-884.
67.
Li S, Liu Y. Identification of vibration loads on hydro generator by using hybrid genetic algorithm. Acta Mechanica Sinica. 2006;22(6):603-610.
68.
Xia Z, Mao K, Wei S, Wang X, Fang Y, Yang S. Application of genetic algorithm support vector regression model to predict damping of cantilever beam with particle damper. Journal of Low Frequency Noise Vibration and Active Control. 2017;36(2):138-147. https://doi.org/10.1177/026309....
69.
Talebitooti R, Torabi M. Identification of tire force characteristics using a Hybrid method. Applied Soft Computing Journal 2016;40:70-85. https://doi.org/10.1016/j.asoc....
70.
Si L, Baier H. An in situ ensemble impact monitoring and identification technique for fiber composite structures under multiple disturbances. Structural Health Monitoring. 2016; 15(3):247-265. https://doi.org/10.1177/147592....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
„Podniesienie poziomu naukowego i poziomu umiędzynarodowienia czasopisma Diagnostyka oraz upowszechniania informacji o wynikach badań lub prac rozwojowych poprzez udział uznanych zagranicznych recenzentów w ocenie publikacji - zadanie finansowane w ramach umowy 745/P-DUN/2017. ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę”
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
