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AUTHOR(S):

Matteo D. L. Dalla Vedova

 

TITLE

Model-Based Prognostic Methods Applied to Physical Dynamic Systems

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ABSTRACT

In several engineering fields, especially in the recent years, the development of adequate diagnostic/prognostic methodologies able to provide a timely and reliable evaluation of the health status of a given system has become a strategic task in order to guarantee suitable levels of reliability, robustness and logistic availability. In particular, at this moment are in the spotlight some prognostic approaches that, on the basis of some representative parameters (measured directly or indirectly), are able to evaluate the health status of a physical system with a suitable (and quantifiable) level of accuracy and robustness; it must be noted that, especially in recent years, these methods are increasingly meeting interest and application in many technical fields and, nowadays, they represent an important task in various scientific disciplines. If considered failures are characterized to progressive evolutions, the health status of a given dynamic system (e.g. environmental, mechatronic, structural, etc.) and the related failure modes can be identified and quantified by means of different approaches widely described in the literature. In the last ten years more and more researchers studied and proposed new strategies aimed to design prognostic algorithms able to identify precursors of the progressive failures affecting a system: in fact, when a degradation pattern is correctly identified, it is possible to trigger an early warning and, if necessary, activate corrective actions (i.e. proper remedial or maintenance tasks, replacement of the damaged components, etc.). Typically these methods are strictly technology-oriented: they can result extremely effective for some specific applications whereas may fail for other purposes and technologies; therefore, it is necessary to "design" and calibrate the prognostic algorithm as a function of the considered problem, taking into account several parameters such as the given (dynamic) system, the available sensors (physical or virtual), the considered progressive failures and the related boundary conditions. This work proposes an overview of the most common model-based diagnostic/prognostic strategies (derived from aerospace systems field), putting in evidence their applicability, strengths and eventual shortcomings.

KEYWORDS

Model-Based Approach, PHM, Prognostics/Diagnostic Algorithms, Physical Dynamic Systems

REFERENCES

[1] W. R. Simpson and J. W. Sheppard, System Test and Diagnosis, Kluwer Academic Publishers, Boston, 1994.

[2] C. S. Byington, W. Watson, D. Edwards and P. Stoelting, A Model-Based Approach to Prognostics and Health Management for Flight Control Actuators, IEEE Aerospace Conference Proceedings, USA, 2004.

[3] G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Wiley, 2006.

[4] M. Battipede, M. D. L. Dalla Vedova, P. Maggiore and S. Romeo, Model based analysis of precursors of electromechanical servo-mechanisms failures using an artificial neural network, AIAA Modeling and Simulation Technologies Conference, Kissimmee, 2015.

[5] L. Borello, M. D. L. Dalla Vedova, G. Jacazio and M. Sorli, A Prognostic Model for Electrohydraulic Servovalves, Annual Conference of the Prognostics and Health Management Society PHM 2009, San Diego, CA, 2009.

[6] A. Raie and V. Rashtchi, Using a genetic algorithm for detection and magnitude determination of turn faults in an induction motor, Electrical Engineering, Vol.84, 2002.

[7] M. Alamyal, S. M. Gadoue and B. Zahawi, Detection of induction machine winding faults using genetic algorithm. Diagnostics for Electric Machines, Power Electronics and Drives 9th IEEE Int.Symposium, Valencia (Spain), 2013, pp. 157-161.

[8] M. D. L. Dalla Vedova, P. Maggiore, L. Pace and A. Desando, Evaluation of the correlation coefficient as a prognostic indicator for electromechanical servomechanism failures, International Journal of Prognostics and Health Management, Vol.6, 2015.

[9] M. S. Mamis, M. Arkan and C. Keles, Transmission lines fault location using transient signal spectrum, International Journal of Electrical Power & Energy Systems , Vol.53, 2013, pp. 714-718.

[10] M. D. L. Dalla Vedova, P. Maggiore and L. Pace, Proposal of prognostic parametric method applied to an electrohydraulic servomechanism affected by multiple failures, WSEAS Transactions on Environment and Development, Vol.10, 2014, pp. 478-490.

[11] M. D. L. Dalla Vedova, G. Jacazio, P. Maggiore, M. Sorli, Identification of precursors of servovalves failures for implementation of an effective prognostics, Int. Conference of Recent Advances in Aerospace Actuation Systems and Components, 2010, pp. 116-126.

[12] S. S. Refaat, H. Abu-Rub, M. S. Saad, E. M. Aboul-Zahab and A. Iqbal, ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal, IEEE International Conference on Industrial Technology (ICIT), 2013, pp. 253-258.

[13] H. Su and K. T. Chong, Induction machine condition monitoring using neural network modelling, IEEE Transactions on Industrial Electronics, Vol.54, No.1, 2007, pp. 241-249.

[14] S. Hamdani, O. Touhami, R. Ibtiouen and M. Fadel, Neural network technique for induction motor rotor faults classification-dynamic eccentricity and broken bar faults, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, 2011, pp. 626-631.

[15] M. D. L. Dalla Vedova, D. De Fano and P. Maggiore, Neural network design for incipient failure detection on aircraft EM actuator, International Journal of Mechanics and Control, Vol.17, No.1, 2016, pp. 77-83.

[16] P. C. Berri, M. D. L. Dalla Vedova, P. Maggiore, A smart electromechanical actuator monitor for new model-based prognostic algorithms, International Journal of Mechanics and Control, Vol.17, No.2, 2016, pp. 19-25.

[17] P. C. Berri, M. D. L. Dalla Vedova and P. Maggiore, On-board electromechanical servomechanisms affected by progressive faults: proposal of a smart GA model-based prognostic approach, 27th European Safety and Reliability Conference, 2017, pp. 839-845.

[18] N. Metropolis, A. N. Rosenbluth, M. N. Rosenbluth, A. H. Teller and E. Teller, Equations of State Calculations by Fast Computing Machines, Journal of Chemical Physics, Vol.21, No.6, 1953, pp. 1087-1092.

[19] V. Miranda, D. Srinivasan and L. Proenca¸ Evolutionary computation in powersystems, Int. Journal of Electr. Power Energy Syst., Vol.20, No.2, 1998, pp. 89-98.

[20] S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi, Optimization by Simulated Annealing, Science, Vol.220, 1983, pp. 671-680.

[21] R. H. J. M. Otten and L. P. P. P. Van Ginneken, The Annealing Algorithm, Kluwer Academic Publishers, Boston, 1989.

[22] M. Subotic and M. Tuba, Parallelized Multiple Swarm Artificial Bee Colony Algorithm (MS-ABC) for Global Optimization, Studies in Informatics and Control, Vol.23, No.1, 2014.

[23] M. Tuba, M. Subotic and N. Stanarevic, Modified cuckoo search algorithm for unconstrained optimization problems, ECC'11 Proc. of the 5th European Conference on European Computing Conference, Paris, France, 2011, pp. 263-268.

[24] M. Subutic, M. Tuba and N. Stanarevic, Parallelization of the firefly algorithm for unconstrained optimization problems, Latest Advances in Information Science and Applications, Vol.22, No.3, 2012, pp. 264-269.

[25] M. Pirlot, General Local Search Methods, European Journal of Operational Research, Vol.92, 1996, pp. 493-511.

[26] T. Jing, C. Morillo and M. G. Pecht, Rolling element bearing fault diagnosis using simulated annealing optimized spectral kurtosis, IEEE Conference on Prognostics and Health Management (PHM), 2013.

[27] K. K. Vishwakarma, H. M. Dubey, M. Pandit and B. K. Panigrahi, Simulated annealing approach for solving economic load dispatch problems with valve point loading effects, International Journal of Engineering, Science and Technology, Vol.4, No.4, 2012, pp. 60-72.

[28] A. Sadegheih, Evolutionary Algorithms and Simulated Annealing in the Topological Configuration of the Spanning Tree, WSEAS Transactions on Systems, Vol.7, No.2, 2008.

[29] C. R. Yu and Y. Luo, An Improved Nested Partitions Algorithm Based on Simulated Annealing in Complex Decision Problem Optimization, WSEAS Transactions on Computers, Vol.7, No.3, 2008, pp. 75-82.

[30] M. D. L. Dalla Vedova, P. Maggiore and L. Pace, A New Prognostic Method Based on Simulated Annealing Algorithm to Deal with the Effects of Dry Friction on Electro mechanical Actuators, International Journal of Mechanics, Vol.9, 2015, pp. 236-245.

[31] M. D. L. Dalla Vedova, D. Lauria, P. Maggiore and L. Pace, Electromechanical actuators affected by multiple failures: A simulated-annealing-based fault identification algorithm, International Journal of Mechanics, Vol.10, 2016, pp. 219-226.

[32] M. Mitchell, An introduction to genetic algorithms, MIT Press, Cambridge, 1996.

[33] M. D. L. Dalla Vedova, A. Germanà and P. Maggiore, Proposal of a new simulated annealing model-based fault identification technique applied to flight control EM actuators, Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016, Glasgow, 2016, pp. 313-321.

[34] R. E. J. Quigley, More electric aircraft, Proc. of the Eighth Annual IEEE Applied Power Electronics Conference - APEC '93, San Diego, CA, 1993, pp. 906-911.

[35] M. Howse, All-electric aircraft, Power Engineer, Vol.17, No.4, 2003.

[36] T.A. Haskew, D.E. Schinstock and E. Waldrep, Two-Phase On’ Drive Operation in a Permanent Magnet Synchronous Machine Electromechanical Actuator, IEEE Transactions on Energy Conversion, Vol.14, 1999.

[37] B. K. Lee and M. Ehsani, Advanced Simulation Model for Brushless DC Motor Drives, Electric Power Components and Systems, Vol.31, No.9, 2003, pp. 841-868.

[38] A. Halvaei Niasar, H. Moghbelli and A. Vahedi, Modelling, Simulation and Implementation of Four-Switch Brushless DC Motor Drive Based On Switching Functions, IEEE EUROCON 2009, 18-23 May, St. Petersburg, Russia, 2009.

[39] M. Çunkas and O. Aydoğdu, Realization of Fuzzy Logic Controlled Brushless DC Motor Drives using Matlab/Simulink, Mathematical and Computational Applications, Vol.15, No.02, 2010, pp. 218-229.

[40] L. Borello, G. Villero and M. D. L. Dalla Vedova, New asymmetry monitoring techniques: effects on attitude control, Aerospace Science and Technology, Vol.13, No.8, 2009, pp. 475-487.

[41] L. Borello and M. D. L. Dalla Vedova, Flaps Failure and Aircraft Controllability: Developments in Asymmetry Monitoring Techniques, Journal of Mechanical Science and Technology (JMST), Vol.28, No.11, 2014, pp. 4593-4603.

[42] L. Borello and M. D. L. Dalla Vedova, A dry friction model and robust computational algorithm for reversible or irreversible motion transmission, International Journal of Mechanics and Control, Vol.13, No.2, 2012, pp. 37-48.

Cite this paper

Matteo D. L. Dalla Vedova. (2018) Model-Based Prognostic Methods Applied to Physical Dynamic Systems. International Journal of Mathematical and Computational Methods, 3, 28-36

 

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