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The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.
Saverio Farsoni; Silvio Simani; Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. Applied Sciences 2021, 11, 5035 .
AMA StyleSaverio Farsoni, Silvio Simani, Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. Applied Sciences. 2021; 11 (11):5035.
Chicago/Turabian StyleSaverio Farsoni; Silvio Simani; Paolo Castaldi. 2021. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." Applied Sciences 11, no. 11: 5035.
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.
Saverio Farsoni; Silvio Simani; Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. 2021, 1 .
AMA StyleSaverio Farsoni, Silvio Simani, Paolo Castaldi. Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis. . 2021; ():1.
Chicago/Turabian StyleSaverio Farsoni; Silvio Simani; Paolo Castaldi. 2021. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." , no. : 1.
This paper addresses the development of an active fault tolerant control scheme for avionic systems. The methodology is applied to an aircraft longitudinal autopilot taking into account possible faults on the aircraft actuators. The key feature of the proposed control relies on its active characteristics, as the fault diagnosis strategy is based on a robust estimate of the fault signals that are compensated. The design method uses an intelligent data–driven scheme via a fuzzy modelling and identification procedure, which derives these adaptive filters with disturbance decoupling features. The work shows that these fault estimates can be used for fault accommodation. In particular, the fuzzy approach proposed in the paper provides the reconstruction of the fault signals that are decoupled from the wind components, and thus applied to the aircraft system. The proposed solutions provide interesting robustness features that are analysed by using a high–fidelity simulator, which is able to include different operating points and realistic actuator faults, turbulence, measurement errors, and the model–reality mismatch.
Silvio Simani; Paolo Castaldi; Saverio Farsoni. Fault Diagnosis and Fault-Tolerant Control for Avionic Systems. Advances in Intelligent Systems and Computing 2020, 191 -201.
AMA StyleSilvio Simani, Paolo Castaldi, Saverio Farsoni. Fault Diagnosis and Fault-Tolerant Control for Avionic Systems. Advances in Intelligent Systems and Computing. 2020; ():191-201.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi; Saverio Farsoni. 2020. "Fault Diagnosis and Fault-Tolerant Control for Avionic Systems." Advances in Intelligent Systems and Computing , no. : 191-201.
The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between track-type and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project. Program Title: gSeaGen CPC Library link to program files: http://dx.doi.org/10.17632/ymgxvy2br4.1 Licensing provisions: GPLv3 Programming language: C++ External routines/libraries: GENIE [1] and its external dependencies. Linkable to MUSIC [2] and PROPOSAL [3]. Nature of problem: Development of a code to generate detectable events in neutrino telescopes, using modern and maintained neutrino interaction simulation libraries which include the state-of-the-art physics models. The default application is the simulation of neutrino interactions within KM3NeT [4]. Solution method: Neutrino interactions are simulated using GENIE, a modern framework for Monte Carlo event generators. The GENIE framework, used by nearly all modern neutrino experiments, is considered as a reference code within the neutrino community. Additional comments including restrictions and unusual features: The code was tested with GENIE version 2.12.10 and it is linkable with release series 3. Presently valid up to 5 TeV. This limitation is not intrinsic to the code but due to the present GENIE valid energy range. References: [1] C. Andreopoulos at al., Nucl. Instrum. Meth. A614 (2010) 87. [2] P. Antonioli et al., Astropart. Phys. 7 (1997) 357. [3] J. H. Koehne et al., Comput. Phys. Commun. 184 (2013) 2070. [4] S. Adrián-Martínez et al., J. Phys. G: Nucl. Part. Phys. 43 (2016) 084001.
S. Aiello; A. Albert; S. Alves Garre; Z. Aly; F. Ameli; M. Andre; G. Androulakis; M. Anghinolfi; M. Anguita; Gisela Anton; M. Ardid; J. Aublin; C. Bagatelas; G. Barbarino; B. Baret; S. Basegmez du Pree; M. Bendahman; E. Berbee; A.M. Van Den Berg; V. Bertin; S. Biagi; A. Biagioni; M. Bissinger; M. Boettcher; J. Boumaaza; M. Bouta; M. Bouwhuis; C. Bozza; H. Brânzaş; M. Bruchner; R. Bruijn; J. Brunner; E. Buis; R. Buompane; J. Busto; D. Calvo; A. Capone; V. Carretero; P. Castaldi; S. Celli; M. Chabab; N. Chau; A. Chen; S. Cherubini; V. Chiarella; T. Chiarusi; M. Circella; R. Cocimano; J.A.B. Coelho; A. Coleiro; M. Colomer Molla; R. Coniglione; I. Corredoira; P. Coyle; A. Creusot; G. Cuttone; A. D’Onofrio; R. Dallier; M. De Palma; I. Di Palma; A.F. Díaz; D. Diego-Tortosa; C. Distefano; A. Domi; R. Donà; C. Donzaud; D. Dornic; M. Dörr; D. Drouhin; M. Durocher; Thomas Eberl; D. van Eijk; I. El Bojaddaini; D. Elsaesser; A. Enzenhöfer; V. Espinosa Roselló; P. Fermani; G. Ferrara; M.D. Filipović; F. Filippini; A. Franco; L.A. Fusco; O. Gabella; T. Gal; Alfonso Andres Garcia Soto; F. Garufi; Y. Gatelet; N. Geißelbrecht; L. Gialanella; E. Giorgio; S.R. Gozzini; R. Gracia; K. Graf; Dario Grasso; G. Grella; D. Guderian; C. Guidi; S. Hallmann; H. Hamdaoui; H. van Haren; A. Heijboer; A. Hekalo; J.J. Hernández-Rey; J. Hofestädt; F. Huang; W. Idrissi Ibnsalih; G. Illuminati; C.W. James; M. de Jong; B.J. Jung; M. Kadler; P. Kalaczyński; O. Kalekin; U.F. Katz; N.R. Khan Chowdhury; F. van der Knaap; E.N. Koffeman; P. Kooijman; A. Kouchner; M. Kreter; V. Kulikovskiy; R. Lahmann; G. Larosa; R. Le Breton; O. Leonardi; F. Leone; E. Leonora; G. Levi; M. Lincetto; M. Lindsey Clark; T. Lipreau; A. Lonardo; F. Longhitano; D. Lopez-Coto; L. Maderer; J. Mańczak; K. Mannheim; A. Margiotta; A. Marinelli; C. Markou; L. Martin; J.A. Martínez-Mora; A. Martini; F. Marzaioli; S. Mastroianni; S. Mazzou; K.W. Melis; G. Miele; P. Migliozzi; E. Migneco; P. Mijakowski; L.S. Miranda; Z. Modebadze; C.M. Mollo; M. Morganti; M. Moser; A. Moussa; R. Muller; M. Musumeci; L. Nauta; S. Navas; C.A. Nicolau; B. Ó Fearraigh; Mukharbek Organokov; A. Orlando; G. Papalashvili; R. Papaleo; C. Pastore; A.M. Paun; G.E. Păvălaş; C. Pellegrino; M. Perrin-Terrin; P. Piattelli; C. Pieterse; K. Pikounis; O. Pisanti; C. Poirè; V. Popa; M. Post; T. Pradier; G. Pühlhofer; S. Pulvirenti; L. Quinn; O. Rabyang; F. Raffaelli; N. Randazzo; A. Rapicavoli; S. Razzaque; D. Real; S. Reck; J. Reubelt; G. Riccobene; M. Richer; S. Rivoire; A. Rovelli; F. Salesa Greus; D.F.E. Samtleben; A. Sánchez Losa; M. Sanguineti; A. Santangelo; D. Santonocito; P. Sapienza; J. Schnabel; V. Sciacca; J. Seneca; I. Sgura; R. Shanidze; A. Sharma; F. Simeone; A. Sinopoulou; B. Spisso; M. Spurio; D. Stavropoulos; J. Steijger; S.M. Stellacci; M. Taiuti; Y. Tayalati; E. Tenllado; T. Thakore; S. Tingay; E. Tzamariudaki; D. Tzanetatos; V. Van Elewyck; G. Vannoye; G. Vasileiadis; F. Versari; S. Viola; D. Vivolo; G. de Wasseige; J. Wilms; R. Wojaczyński; E. de Wolf; D. Zaborov; S. Zavatarelli; A. Zegarelli; J.D. Zornoza; J. Zúñiga; N. Zywucka. gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes. Computer Physics Communications 2020, 256, 107477 .
AMA StyleS. Aiello, A. Albert, S. Alves Garre, Z. Aly, F. Ameli, M. Andre, G. Androulakis, M. Anghinolfi, M. Anguita, Gisela Anton, M. Ardid, J. Aublin, C. Bagatelas, G. Barbarino, B. Baret, S. Basegmez du Pree, M. Bendahman, E. Berbee, A.M. Van Den Berg, V. Bertin, S. Biagi, A. Biagioni, M. Bissinger, M. Boettcher, J. Boumaaza, M. Bouta, M. Bouwhuis, C. Bozza, H. Brânzaş, M. Bruchner, R. Bruijn, J. Brunner, E. Buis, R. Buompane, J. Busto, D. Calvo, A. Capone, V. Carretero, P. Castaldi, S. Celli, M. Chabab, N. Chau, A. Chen, S. Cherubini, V. Chiarella, T. Chiarusi, M. Circella, R. Cocimano, J.A.B. Coelho, A. Coleiro, M. Colomer Molla, R. Coniglione, I. Corredoira, P. Coyle, A. Creusot, G. Cuttone, A. D’Onofrio, R. Dallier, M. De Palma, I. Di Palma, A.F. Díaz, D. Diego-Tortosa, C. Distefano, A. Domi, R. Donà, C. Donzaud, D. Dornic, M. Dörr, D. Drouhin, M. Durocher, Thomas Eberl, D. van Eijk, I. El Bojaddaini, D. Elsaesser, A. Enzenhöfer, V. Espinosa Roselló, P. Fermani, G. Ferrara, M.D. Filipović, F. Filippini, A. Franco, L.A. Fusco, O. Gabella, T. Gal, Alfonso Andres Garcia Soto, F. Garufi, Y. Gatelet, N. Geißelbrecht, L. Gialanella, E. Giorgio, S.R. Gozzini, R. Gracia, K. Graf, Dario Grasso, G. Grella, D. Guderian, C. Guidi, S. Hallmann, H. Hamdaoui, H. van Haren, A. Heijboer, A. Hekalo, J.J. Hernández-Rey, J. Hofestädt, F. Huang, W. Idrissi Ibnsalih, G. Illuminati, C.W. James, M. de Jong, B.J. Jung, M. Kadler, P. Kalaczyński, O. Kalekin, U.F. Katz, N.R. Khan Chowdhury, F. van der Knaap, E.N. Koffeman, P. Kooijman, A. Kouchner, M. Kreter, V. Kulikovskiy, R. Lahmann, G. Larosa, R. Le Breton, O. Leonardi, F. Leone, E. Leonora, G. Levi, M. Lincetto, M. Lindsey Clark, T. Lipreau, A. Lonardo, F. Longhitano, D. Lopez-Coto, L. Maderer, J. Mańczak, K. Mannheim, A. Margiotta, A. Marinelli, C. Markou, L. Martin, J.A. Martínez-Mora, A. Martini, F. Marzaioli, S. Mastroianni, S. Mazzou, K.W. Melis, G. Miele, P. Migliozzi, E. Migneco, P. Mijakowski, L.S. Miranda, Z. Modebadze, C.M. Mollo, M. Morganti, M. Moser, A. Moussa, R. Muller, M. Musumeci, L. Nauta, S. Navas, C.A. Nicolau, B. Ó Fearraigh, Mukharbek Organokov, A. Orlando, G. Papalashvili, R. Papaleo, C. Pastore, A.M. Paun, G.E. Păvălaş, C. Pellegrino, M. Perrin-Terrin, P. Piattelli, C. Pieterse, K. Pikounis, O. Pisanti, C. Poirè, V. Popa, M. Post, T. Pradier, G. Pühlhofer, S. Pulvirenti, L. Quinn, O. Rabyang, F. Raffaelli, N. Randazzo, A. Rapicavoli, S. Razzaque, D. Real, S. Reck, J. Reubelt, G. Riccobene, M. Richer, S. Rivoire, A. Rovelli, F. Salesa Greus, D.F.E. Samtleben, A. Sánchez Losa, M. Sanguineti, A. Santangelo, D. Santonocito, P. Sapienza, J. Schnabel, V. Sciacca, J. Seneca, I. Sgura, R. Shanidze, A. Sharma, F. Simeone, A. Sinopoulou, B. Spisso, M. Spurio, D. Stavropoulos, J. Steijger, S.M. Stellacci, M. Taiuti, Y. Tayalati, E. Tenllado, T. Thakore, S. Tingay, E. Tzamariudaki, D. Tzanetatos, V. Van Elewyck, G. Vannoye, G. Vasileiadis, F. Versari, S. Viola, D. Vivolo, G. de Wasseige, J. Wilms, R. Wojaczyński, E. de Wolf, D. Zaborov, S. Zavatarelli, A. Zegarelli, J.D. Zornoza, J. Zúñiga, N. Zywucka. gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes. Computer Physics Communications. 2020; 256 ():107477.
Chicago/Turabian StyleS. Aiello; A. Albert; S. Alves Garre; Z. Aly; F. Ameli; M. Andre; G. Androulakis; M. Anghinolfi; M. Anguita; Gisela Anton; M. Ardid; J. Aublin; C. Bagatelas; G. Barbarino; B. Baret; S. Basegmez du Pree; M. Bendahman; E. Berbee; A.M. Van Den Berg; V. Bertin; S. Biagi; A. Biagioni; M. Bissinger; M. Boettcher; J. Boumaaza; M. Bouta; M. Bouwhuis; C. Bozza; H. Brânzaş; M. Bruchner; R. Bruijn; J. Brunner; E. Buis; R. Buompane; J. Busto; D. Calvo; A. Capone; V. Carretero; P. Castaldi; S. Celli; M. Chabab; N. Chau; A. Chen; S. Cherubini; V. Chiarella; T. Chiarusi; M. Circella; R. Cocimano; J.A.B. Coelho; A. Coleiro; M. Colomer Molla; R. Coniglione; I. Corredoira; P. Coyle; A. Creusot; G. Cuttone; A. D’Onofrio; R. Dallier; M. De Palma; I. Di Palma; A.F. Díaz; D. Diego-Tortosa; C. Distefano; A. Domi; R. Donà; C. Donzaud; D. Dornic; M. Dörr; D. Drouhin; M. Durocher; Thomas Eberl; D. van Eijk; I. El Bojaddaini; D. Elsaesser; A. Enzenhöfer; V. Espinosa Roselló; P. Fermani; G. Ferrara; M.D. Filipović; F. Filippini; A. Franco; L.A. Fusco; O. Gabella; T. Gal; Alfonso Andres Garcia Soto; F. Garufi; Y. Gatelet; N. Geißelbrecht; L. Gialanella; E. Giorgio; S.R. Gozzini; R. Gracia; K. Graf; Dario Grasso; G. Grella; D. Guderian; C. Guidi; S. Hallmann; H. Hamdaoui; H. van Haren; A. Heijboer; A. Hekalo; J.J. Hernández-Rey; J. Hofestädt; F. Huang; W. Idrissi Ibnsalih; G. Illuminati; C.W. James; M. de Jong; B.J. Jung; M. Kadler; P. Kalaczyński; O. Kalekin; U.F. Katz; N.R. Khan Chowdhury; F. van der Knaap; E.N. Koffeman; P. Kooijman; A. Kouchner; M. Kreter; V. Kulikovskiy; R. Lahmann; G. Larosa; R. Le Breton; O. Leonardi; F. Leone; E. Leonora; G. Levi; M. Lincetto; M. Lindsey Clark; T. Lipreau; A. Lonardo; F. Longhitano; D. Lopez-Coto; L. Maderer; J. Mańczak; K. Mannheim; A. Margiotta; A. Marinelli; C. Markou; L. Martin; J.A. Martínez-Mora; A. Martini; F. Marzaioli; S. Mastroianni; S. Mazzou; K.W. Melis; G. Miele; P. Migliozzi; E. Migneco; P. Mijakowski; L.S. Miranda; Z. Modebadze; C.M. Mollo; M. Morganti; M. Moser; A. Moussa; R. Muller; M. Musumeci; L. Nauta; S. Navas; C.A. Nicolau; B. Ó Fearraigh; Mukharbek Organokov; A. Orlando; G. Papalashvili; R. Papaleo; C. Pastore; A.M. Paun; G.E. Păvălaş; C. Pellegrino; M. Perrin-Terrin; P. Piattelli; C. Pieterse; K. Pikounis; O. Pisanti; C. Poirè; V. Popa; M. Post; T. Pradier; G. Pühlhofer; S. Pulvirenti; L. Quinn; O. Rabyang; F. Raffaelli; N. Randazzo; A. Rapicavoli; S. Razzaque; D. Real; S. Reck; J. Reubelt; G. Riccobene; M. Richer; S. Rivoire; A. Rovelli; F. Salesa Greus; D.F.E. Samtleben; A. Sánchez Losa; M. Sanguineti; A. Santangelo; D. Santonocito; P. Sapienza; J. Schnabel; V. Sciacca; J. Seneca; I. Sgura; R. Shanidze; A. Sharma; F. Simeone; A. Sinopoulou; B. Spisso; M. Spurio; D. Stavropoulos; J. Steijger; S.M. Stellacci; M. Taiuti; Y. Tayalati; E. Tenllado; T. Thakore; S. Tingay; E. Tzamariudaki; D. Tzanetatos; V. Van Elewyck; G. Vannoye; G. Vasileiadis; F. Versari; S. Viola; D. Vivolo; G. de Wasseige; J. Wilms; R. Wojaczyński; E. de Wolf; D. Zaborov; S. Zavatarelli; A. Zegarelli; J.D. Zornoza; J. Zúñiga; N. Zywucka. 2020. "gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes." Computer Physics Communications 256, no. : 107477.
The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.
Silvio Simani; Paolo Castaldi. Fault Diagnosis Techniques for a Wind Turbine System. Fault Detection, Diagnosis and Prognosis 2020, 1 .
AMA StyleSilvio Simani, Paolo Castaldi. Fault Diagnosis Techniques for a Wind Turbine System. Fault Detection, Diagnosis and Prognosis. 2020; ():1.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi. 2020. "Fault Diagnosis Techniques for a Wind Turbine System." Fault Detection, Diagnosis and Prognosis , no. : 1.
Cortical responses to external mechanical stimuli recorded by electroencephalography have demonstrated complex nonlinearity with fast dynamics. Hence, the modelling of the human nervous system plays a crucial role in studying the function of the sensorimotor system and can help in disentangling the sensory-motor abnormalities in functional movement disorders. In this paper, a non-parametric model is estimated based on locally-linear neuro-fuzzy structures trained by an evolutive algorithm relying on locally-linear model-tree. In particular, simulation model as well as a multi-step ahead predictor model is considered to describe the nonlinear dynamics governing the cortical response. The proposed modelling method is applied to an experimental dataset representing brain activities from ten young healthy subjects. These electroencephalography signals are recorded while robotic manipulations have been applied to their wrist joints. The obtained results are satisfactory and are also compared to those achieved with different modelling strategies applied to the same benchmark data.
Hasan A. Nozari; Z. Rahmani; Paolo Castaldi; S. Simani; S.J. Sadati. Data-Driven Modelling of the Nonlinear Cortical Responses Generated by Continuous Mechanical Perturbations. IFAC-PapersOnLine 2020, 53, 322 -327.
AMA StyleHasan A. Nozari, Z. Rahmani, Paolo Castaldi, S. Simani, S.J. Sadati. Data-Driven Modelling of the Nonlinear Cortical Responses Generated by Continuous Mechanical Perturbations. IFAC-PapersOnLine. 2020; 53 (2):322-327.
Chicago/Turabian StyleHasan A. Nozari; Z. Rahmani; Paolo Castaldi; S. Simani; S.J. Sadati. 2020. "Data-Driven Modelling of the Nonlinear Cortical Responses Generated by Continuous Mechanical Perturbations." IFAC-PapersOnLine 53, no. 2: 322-327.
The aerospace engineering educational system aims to create future professionals able to solve problems of high complexity, with time constraints and which solutions matches prescribed level of performance. In our past work, we introduced the innovative concept of the Professional Readiness Level (PRL) as a unique parameter to quantify how close the students are to the aerospace industry. In this paper we propose a dynamic model, of the PRL, capable to capture, in simple but effective way, the student behaviour we, as professors, observed in our educative experience.
P. Castaldi; N. Mimmo. A Mathematical Model in Automatic Control Aerospace Engineering Education. IFAC-PapersOnLine 2020, 53, 17138 -17143.
AMA StyleP. Castaldi, N. Mimmo. A Mathematical Model in Automatic Control Aerospace Engineering Education. IFAC-PapersOnLine. 2020; 53 (2):17138-17143.
Chicago/Turabian StyleP. Castaldi; N. Mimmo. 2020. "A Mathematical Model in Automatic Control Aerospace Engineering Education." IFAC-PapersOnLine 53, no. 2: 17138-17143.
This paper suggests a model-free framework for Fault Detection and Isolation (FDI) of satellite reaction wheels for the first time. The proposed FDI method is based on multi-classifier fusion with diverse learning algorithms and configured in a parallel form where a unique module simultaneously performs both detection and isolation tasks. In other words, a multi-classifier-based arrangement is presented on the basis of Mixed Learning strategy where four classic and well-practised classification schemes including Random Forest, Support Vector Machine, Partial Least Square, and Naïve Bayes are incorporated into FDI module in order to make a decision on the occurrence of a fault and its location. Extensive simulation results with a high-fidelity nonlinear spacecraft simulator considering gyroscopic effects, measurement noise, and exogenous aerodynamic disturbance signals show that the proposed FDI scheme can cope with faults affecting reaction wheel torques and obtain promising FDI performances in most of the designed scenarios.
Hasan Abbasi Nozari; Paolo Castaldi; Hamed Dehghan Banadaki; Silvio Simani. Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework. IFAC-PapersOnLine 2019, 52, 194 -199.
AMA StyleHasan Abbasi Nozari, Paolo Castaldi, Hamed Dehghan Banadaki, Silvio Simani. Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework. IFAC-PapersOnLine. 2019; 52 (12):194-199.
Chicago/Turabian StyleHasan Abbasi Nozari; Paolo Castaldi; Hamed Dehghan Banadaki; Silvio Simani. 2019. "Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework." IFAC-PapersOnLine 52, no. 12: 194-199.
This paper presents an attitude active fault tolerant control for a satellite simulated model. The proposed fault tolerant controller consists of two main parts: a nominal controller, designed assuming that the system is healthy, and a fault diagnosis module which aims at detecting, isolating and estimating the fault affecting the system. The fault diagnosis module is designed by using the non-linear geometric approach tool which allows to obtain detection residuals and estimation filter decoupled from the aerodynamic disturbance representing the main uncertainty in low Earth orbits. The simulation results show the advantages, in terms of attitude tracking accuracy, obtainable when implementing the proposed method.
Paolo Castaldi; Nicola Mimmo; Silvio Simani. LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis. European Journal of Control 2019, 51, 76 -94.
AMA StylePaolo Castaldi, Nicola Mimmo, Silvio Simani. LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis. European Journal of Control. 2019; 51 ():76-94.
Chicago/Turabian StylePaolo Castaldi; Nicola Mimmo; Silvio Simani. 2019. "LEO satellite active FTC with aerodynamic disturbance decoupled fault diagnosis." European Journal of Control 51, no. : 76-94.
Integrity of signals is an important issue for aerospace navigation systems and, in particular, for satellite navigation positioning. In this paper integrity monitoring techniques are processed with a new FDI technique implemented by a snapshot RAIM algorithm, based on linearized models, and position domain tests. The approach consists of the joint exploitation, in an Errors In Variables (EIV) framework, of all the possible Least Squares (LS) solutions under the hypothesis of a single fault on a pseudorange measurement. The characteristics of the behavior of the different LS position and bias estimates, by varying the fault size and the faulty satellite, are investigated. The non linear dependence of the locus of the solutions from the fault size is considered. The analysis of the loci properties results in a new criterion and algorithm for the detection and isolation of a faulty satellite signal. The effectiveness of the proposed method has been compared with respect to a classic FDI method by means of Montecarlo simulations based on a Galileo constellation simulator.
Paolo Castaldi; Matteo Zanzi. A new method for satellite navigation signals FDI. 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) 2019, 601 -606.
AMA StylePaolo Castaldi, Matteo Zanzi. A new method for satellite navigation signals FDI. 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace). 2019; ():601-606.
Chicago/Turabian StylePaolo Castaldi; Matteo Zanzi. 2019. "A new method for satellite navigation signals FDI." 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) , no. : 601-606.
This paper addresses the fault diagnosis problem of sensors of an aeronautical system based on GNSS/IMU. The model considered is a rigid body with 6DoF. Considering its linearized kinematic model, the proposed FDI scheme is based on a generalized bank of observers (GBO) designed with the UIO theory. The fault scenario is composed by multiple but not concurrent faults without any hypothesis on the kind of fault. The structural conditions for fault diagnosability are analytically demonstrated.
Paolo Castaldi; N. Mimmo; M. Menghini. Diagnosability of GNSS/IMU System without Hardware Redundancy. 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) 2019, 516 -521.
AMA StylePaolo Castaldi, N. Mimmo, M. Menghini. Diagnosability of GNSS/IMU System without Hardware Redundancy. 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace). 2019; ():516-521.
Chicago/Turabian StylePaolo Castaldi; N. Mimmo; M. Menghini. 2019. "Diagnosability of GNSS/IMU System without Hardware Redundancy." 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) , no. : 516-521.
Paolo Castaldi; A. Macchelli; N. Mimmo. Detectability Analysis of Faults Affecting Actuators and Sensors of Flexible Space Structures. 2019 18th European Control Conference (ECC) 2019, 1 .
AMA StylePaolo Castaldi, A. Macchelli, N. Mimmo. Detectability Analysis of Faults Affecting Actuators and Sensors of Flexible Space Structures. 2019 18th European Control Conference (ECC). 2019; ():1.
Chicago/Turabian StylePaolo Castaldi; A. Macchelli; N. Mimmo. 2019. "Detectability Analysis of Faults Affecting Actuators and Sensors of Flexible Space Structures." 2019 18th European Control Conference (ECC) , no. : 1.
Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.
Silvio Simani; Paolo Castaldi. Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System. Applied Sciences 2019, 9, 783 .
AMA StyleSilvio Simani, Paolo Castaldi. Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System. Applied Sciences. 2019; 9 (4):783.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi. 2019. "Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System." Applied Sciences 9, no. 4: 783.
Paolo Castaldi; Nicola Mimmo. An Experience of Project Based Learning in Aerospace Engineering. IFAC-PapersOnLine 2019, 52, 484 -489.
AMA StylePaolo Castaldi, Nicola Mimmo. An Experience of Project Based Learning in Aerospace Engineering. IFAC-PapersOnLine. 2019; 52 (12):484-489.
Chicago/Turabian StylePaolo Castaldi; Nicola Mimmo. 2019. "An Experience of Project Based Learning in Aerospace Engineering." IFAC-PapersOnLine 52, no. 12: 484-489.
Giulia Villani; Paolo Castaldi; Attilio Toscano; Camilla Stanghellini; Tullio Salmon Cinotti; Rodrigo Filev Maia; Fausto Tomei; Markus Taumberger; Paola Zanetti; Stefano Panizzi. Soil Water Balance Model CRITERIA-ID in SWAMP Project: Proof of Concept. 2018 23rd Conference of Open Innovations Association (FRUCT) 2018, 1 .
AMA StyleGiulia Villani, Paolo Castaldi, Attilio Toscano, Camilla Stanghellini, Tullio Salmon Cinotti, Rodrigo Filev Maia, Fausto Tomei, Markus Taumberger, Paola Zanetti, Stefano Panizzi. Soil Water Balance Model CRITERIA-ID in SWAMP Project: Proof of Concept. 2018 23rd Conference of Open Innovations Association (FRUCT). 2018; ():1.
Chicago/Turabian StyleGiulia Villani; Paolo Castaldi; Attilio Toscano; Camilla Stanghellini; Tullio Salmon Cinotti; Rodrigo Filev Maia; Fausto Tomei; Markus Taumberger; Paola Zanetti; Stefano Panizzi. 2018. "Soil Water Balance Model CRITERIA-ID in SWAMP Project: Proof of Concept." 2018 23rd Conference of Open Innovations Association (FRUCT) , no. : 1.
In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme.
Silvio Simani; Paolo Castaldi. Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation. IFAC-PapersOnLine 2018, 51, 52 -59.
AMA StyleSilvio Simani, Paolo Castaldi. Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation. IFAC-PapersOnLine. 2018; 51 (24):52-59.
Chicago/Turabian StyleSilvio Simani; Paolo Castaldi. 2018. "Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation." IFAC-PapersOnLine 51, no. 24: 52-59.
This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.
Silvio Simani; Saverio Farsoni; Paolo Castaldi. Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark. International Journal of Applied Mathematics and Computer Science 2018, 28, 247 -268.
AMA StyleSilvio Simani, Saverio Farsoni, Paolo Castaldi. Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark. International Journal of Applied Mathematics and Computer Science. 2018; 28 (2):247-268.
Chicago/Turabian StyleSilvio Simani; Saverio Farsoni; Paolo Castaldi. 2018. "Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark." International Journal of Applied Mathematics and Computer Science 28, no. 2: 247-268.
P. Baldi; Paolo Castaldi; Nicola Mimmo; S. Simani. Satellite Attitude Fault Tolerant Control with Aerodynamic Disturbance Decoupling. 2018 European Control Conference (ECC) 2018, 1 .
AMA StyleP. Baldi, Paolo Castaldi, Nicola Mimmo, S. Simani. Satellite Attitude Fault Tolerant Control with Aerodynamic Disturbance Decoupling. 2018 European Control Conference (ECC). 2018; ():1.
Chicago/Turabian StyleP. Baldi; Paolo Castaldi; Nicola Mimmo; S. Simani. 2018. "Satellite Attitude Fault Tolerant Control with Aerodynamic Disturbance Decoupling." 2018 European Control Conference (ECC) , no. : 1.
This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that aid detection, isolation, and estimation of possible actuator and sensor faults. The adaptive observers do not need a priori information about fault internal models. A nonlinear geometric approach is used to avoid that aerodynamic disturbance torques have unwanted influence on the fault estimates. An augmented high‐fidelity spacecraft model is exploited during design and validation to replicate faults. This simulation model includes disturbance torques as experienced in low Earth orbits. This paper includes an analysis to assess robustness properties of the method with respect to parameter uncertainties and disturbances. The results document the efficacy of the suggested methodology.
P. Baldi; Mogens Blanke; P. Castaldi; Nicola Mimmo; S. Simani. Fault diagnosis for satellite sensors and actuators using nonlinear geometric approach and adaptive observers. International Journal of Robust and Nonlinear Control 2018, 29, 5429 -5455.
AMA StyleP. Baldi, Mogens Blanke, P. Castaldi, Nicola Mimmo, S. Simani. Fault diagnosis for satellite sensors and actuators using nonlinear geometric approach and adaptive observers. International Journal of Robust and Nonlinear Control. 2018; 29 (16):5429-5455.
Chicago/Turabian StyleP. Baldi; Mogens Blanke; P. Castaldi; Nicola Mimmo; S. Simani. 2018. "Fault diagnosis for satellite sensors and actuators using nonlinear geometric approach and adaptive observers." International Journal of Robust and Nonlinear Control 29, no. 16: 5429-5455.
This paper presents a combined data-driven framework for fault detection and isolation (FDI) based on the ensemble of diverse classification schemes. The proposed FDI scheme is configured in series and parallel forms in the sense that in series form the decision on the occurrence of fault is made in FD module, and subsequently, the FI module coupled to the FD module will be activated for fault indication purposes. On the other hand, in parallel form a single module is employed for FDI purposes, simultaneously. In other words, two separate multiple-classifiers schemes are presented by using fourteen various statistical and non-statistical classification schemes. Furthermore, in this study, a novel ensemble classification scheme namely blended learning (BL) is proposed for the first time where single and boosted classifiers are blended as the local classifiers in order to enrich the classification performance. Single-classifier schemes are also exploited in FDI modules along with the ensemble-classifier methods for comparison purposes. In order to show the performance of proposed FDI method, it was also tested and validated on DAMADICS actuator system benchmark. Besides, comparative study with the related works done on this benchmark is provided to show the pros and cons of the proposed FDI method.
Hasan Abbasi Nozari; Sina Nazeri; Hamed Dehghan Banadaki; Paolo Castaldi. Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study. Control Engineering Practice 2018, 73, 134 -148.
AMA StyleHasan Abbasi Nozari, Sina Nazeri, Hamed Dehghan Banadaki, Paolo Castaldi. Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study. Control Engineering Practice. 2018; 73 ():134-148.
Chicago/Turabian StyleHasan Abbasi Nozari; Sina Nazeri; Hamed Dehghan Banadaki; Paolo Castaldi. 2018. "Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study." Control Engineering Practice 73, no. : 134-148.