Recent Publications

• M, Döhler, L. Mevel. Subspace-based fault detection robust to changes in the noise covariances . Automatica , 2013. (Abstract | Bibtex )
  The detection of changes in the eigenstructure of a linear time invariant system by means of a subspace-based residual function has been proposed previously. While enjoying some success in its applicability in particular in the context of vibration monitoring, the robustness of this framework against changes in the noise properties has not been properly addressed yet. In this paper, a new robust residual is proposed and the robustness of its statistics against changes in the noise covariances is shown. The complete theory for hypothesis testing for fault detection is derived and a numerical illustration is provided.
  @article{tac12,
      Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Subspace-based fault detection robust to changes in the noise covariances},
      Journal = {Automatica},
      Volume = {49},
      Number = {9},
      Month = {September},
      Year = {2013}
  }
• M, Döhler, L. Mevel. Modular Subspace-Based System Identification from Multi-Setup Measurements . Transaction on Automatic Control , 2012. (Abstract | Bibtex )
  Subspace identification algorithms are efficient for output-only eigenstructure identification of linear MIMO systems. The problem of merging sensor data obtained from moving and nonsimultaneously recorded measurement setups under varying excitation is considered. To address the problem of dimension explosion, when retrieving the system matrices of the complete system, a modular and scalable approach is proposed. Adapted to a large class of subspace methods, observability matrices are normalized and merged to retrieve global system matrices.
  @article{tac12,
      Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Modular Subspace-Based System Identification from Multi-Setup Measurements },
      Journal = {Transaction on Automatic Control},
      Volume = {57},
      Number = {11},
      Month = {November},
      Year = {2012}
  }
• M, Döhler, L. Mevel, F. Hille. Subspace-Based Damage Detection under Changes in the Ambient Excitation Statistics . Mechanical Systems and Signal Processing , 2014. (Abstract | Bibtex )
  In the last ten years, monitoring the integrity of the civil infrastructure has been an active research topic, including in connected areas as automatic control. It is common practice to perform damage detection by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state from measured vibration data. Subspace methods enjoy some popularity in structural engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, a subspacebased fault detection residual has been recently proposed and applied successfully, where the estimation of the modal parameters in the possibly damaged state is avoided. However, most works assume that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. This paper addresses the problem of robustness of such fault detection methods. It is explained why current algorithms from literature fail when the excitation covariance changes and how they can be modified. Then, an efficient and fast subspace-based damage detection test is derived that is robust to changes in the excitation covariance but also to numerical instabilities that can arise easily in the computations. Three numerical applications show the efficiency of the method.
  @article{tac12,
      Author = {Döhler, Michael and Mevel, Laurent and Hille, Falk},
      Title = {Subspace-Based Damage Detection under Changes in the Ambient Excitation Statistics},
      Journal = {Mechanical Systems and Signal Processing},
      Volume = {45},
      Number = {1},
      Month = {March},
      Year = {2014}
  }
• A. Jhinaoui, L. Mevel, J. Morlier. A new SSI algorithm for LPTV systems: application to a hinged-bladed helicopter . Mechanical Systems and Signal Processing, 2014. (Abstract | Bibtex )
  Many systems such as turbo-generators, wind turbines and helicopters show intrinsic time-periodic behaviors. Usually, these structures are considered to be faithfully modeled as Linear Time-Invariant (LTI). In some cases where the rotor is anisotropic, this modeling does not hold and the equations of motion lead necessarily to a Linear Periodically Time-Varying (referred to as LPTV in the control and digital signal eld or LTP in the mechanical and nonlinear dynamics world) model. Classical modal analysis methodologies based on the classical time-invariant eigenstructure (frequencies and damping ratios) of the system no more apply. This is the case in particular for subspace methods. For such time-periodic systems, the modal analysis can be described by characteristic exponents called Floquet multipliers. The aim of this paper is to suggest a new subspace-based algorithm that is able to extract these multipliers and the corresponding frequencies and damping ratios. The algorithm is then tested on a numerical model of a hinged-bladed helicopter on the ground.
  @article{tac12,
      Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Subspace-based fault detection robust to changes in the noise covariances},
      Journal = {Mechanical Systems and Signal Processing},
      Volume = {42},
      Number = {1},
      Month = {January},
      Year = {2014}
  }
• M, Döhler, L. Mevel. Efficient Multi-Order Uncertainty Computation for Stochastic Subspace Identification. Mechanical Systems and Signal Processing , 2013. (Abstract | Bibtex )
  Stochastic Subspace Identification methods have been extensively used for the modal analysis of mechanical, civil or aeronautical structures for the last ten years. So-called stabilization diagrams are used, where modal parameters are estimated at successive model orders, leading to a graphical procedure where the physical modes of the system are extracted and separated from spurious modes. Recently an uncertainty computation scheme has been derived allowing the computation of uncertainty bounds for modal parameters at some given model order. In this paper, two problems are addressed. Firstly, a fast computation scheme is proposed reducing the computational burden of the uncertainty computation scheme by an order of magnitude in the model order compared to a direct implementation. Secondly, a new algorithm is proposed to derive eciently the uncertainty bounds for the estimated modes at all model orders in the stabilization diagram. It is shown that this new algorithm is both computationally and memory ecient, reducing the computational burden by two orders of magnitude in the model order.
  @article{mssp13-1,
      Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Efficient Multi-Order Uncertainty Computation for Stochastic Subspace Identification},
      Journal = {Mechanical Systems and Signal Processing},
      Volume = {38},
      Number = {2},
  Pages = {346--366},
      Month = {June},
      Year = {2013}
  }
• M, Döhler, X. Lam, L. Mevel. Uncertainty Quantification for Modal Parameters from Stochastic Subspace Identification on Multi-Setup Measurements . Mechanical Systems and Signal Processing , 2013. (Abstract | Bibtex )
  In operational modal analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained with stochastic subspace identification from ambient vibration measurements of structures, are subject to statistical uncertainty. It is hence necessary to evaluate the uncertainty bounds of the obtained results, which can be done by a first-order perturbation analysis. To obtain vibration measurements at many coordinates of a structure with only a few sensors, it is common practice to use multiple sensor setups for the measurements. Recently, a multi-setup subspace identification algorithm has been proposed that merges the data from different setups prior to the identification step to obtain one set of global modal parameters, taking the possibly different ambient excitation characteristics between the measurements into account. In this paper, an algorithm is proposed that efficiently estimates the covariances on modal parameters obtained from this multi-setup subspace identication. The new algorithm is validated on multi-setup ambient vibration data of the Z24 Bridge, benchmark of the COST F3 European network.
  @article{mssp13-1,
      Author = {Döhler, Michael and Lam, Xuan-Binh and Mevel, Laurent},
      Title = {Uncertainty Quantification for Modal Parameters from Stochastic Subspace Identification on Multi-Setup Measurements},
      Journal = {Mechanical Systems and Signal Processing},
      Volume = {36},
      Number = {2},
      Pages = {562-581},
      Month = {april},
      Year = {2013}
  }
• M, Zghal, L. Mevel, P. Del Moral. Modal parameter estimation using interacting Kalman filter. Mechanical Systems and Signal Processing , , 2014. (Abstract | Bibtex )
  The focus of this paper is Bayesian modal parameter recursive estimation based on an interacting Kalman filter algorithm with decoupled distributions for frequency and damping. Interacting Kalman filter is a combination of two widely used Bayesian estimation methods: the particle filter and the Kalman filter. Some sensitivity analysis techniques are also proposed in order to deduce a recursive estimate of modal parameters from the estimates of the damping/stiffness coeffcients.
  @article{mssp13-2,
      Author = {Zghal, Meriem and Mevel, Laurent, and Del Moral Pierre},
      Title = {Modal parameter estimation using interacting Kalman filter},
      Journal = {Mechanical Systems and Signal Processing},
  Volume = {49},
  Number = {3},
  Month = {May},
  Year = {2012}
  }
• A, Ashari, L. Mevel. Auxiliary input design for stochastic subspace-based structural damage detection . Mechanical Systems and Signal Processing , 2013. (Abstract | Bibtex )
  This paper considers the problem of auxiliary input design for subspace-based fault detection methods. In several real applications, particularly in the damage detection of mechanical structures and vibrating systems, environment noise is the only input to the system. In some applications, white noise produces low quality output data for the subspace-based fault detection method. In those methods, a residual is calculated to detect the fault based on the output information. However, some modes of the system may not influence the outputs and the residual appropriately if the input is not exciting enough for those modes. In this paper, the method of "rotated inputs" is implemented to excite the system modes. In addition to produce a residual more sensitive to the weak modes, it is possible to detect system order changes due to the fault using the rotated inputs. Simulation results demonstrate the eciency of injecting the auxiliary input to improve the subspace-based fault detection methodology.
  @article{asha13,
      Author = {Ashari, Alireza and Mevel, Laurent},
      Title = {Auxiliary input design for stochastic subspace-based structural damage detection },
      Journal = {Mechanical Systems and Signal Processing},
  VOLUME = {34},
  NUMBER = {1},
  MONTH = jan,
  PAGES = {241-258},
  URL={http://dx.doi.org/10.1016/j.ymssp.2012.08.009},
      Year = {2013}
  }
• M, Döhler, L. Mevel. Fast Multi-Order Computation of System Matrices in Subspace-Based System Identification . Control Engineering Practice , 2012. (Abstract | Bibtex )
  Subspace methods have proven to be efficient for the identification of linear time-invariant systems, especially applied to mechanical, civil or aeronautical structures in operation conditions. Therein, system identification results are needed at multiple (over-specified) model orders in order to distinguish the true structural modes from spurious modes using the so-called stabilization diagrams. In this paper, new efficient algorithms are derived for this multi-order system identification with subspace-based identification algorithms and the closely related Eigensystem Realization Algorithm. It is shown that the new algorithms are significantly faster than the conventional algorithms in use. They are demonstrated on the system identification of a large-scale civil structure.
  @article{cep12,
      Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Fast Multi-Order Computation of System Matrices in Subspace-Based System Identification },
      Journal = {Control Engineering Practice},
      Volume = {20},
      Number = {9},
      Pages = {882--894},
      Month = {September},
      Year = {2012}
  }
• L. Marin, M. Döhler , D. Bernal, L. Mevel. Robust statistical damage localization with stochastic load vectors. Structural Control and Health Monitoring, 2015. (Abstract | Bibtex )
  The stochastic dynamic damage locating vector approach is a vibration-based damage localization method based on a finite element model of a structure and output-only measurements in both reference and damaged states. A stress field is computed for loads in the null space of a surrogate of the change in the transfer matrix at the sensor positions for some values in the Laplace domain. Then, the damage location is related to positions where the stress is close to zero. Robustness of the localization information can be achieved by aggregating results at different values in the Laplace domain. So far, this approach, and in particular the aggregation, is deterministic and does not take the uncertainty in the stress estimates into account. In this paper, the damage localization method is extended with a statistical framework. The uncertainty in the output-only measurements is propagated to the stress estimates at different values of the Laplace variable, and these estimates are aggregated based on statistical principles. The performance of the new statistical approach is demonstrated both in a numerical application and a lab experiment, showing a significant improvement of the robustness of the method due to the statistical evaluation of the localization information.
  @article{mssp13-3,
      Author = { Marin, Luciano and Döhler, Michae and Bernal, Dionisio, and Mevel, Laurent},
      Title = {Robust statistical damage localization with stochastic load vectors},
      Journal = {Structural Control and Health Monitoring},
      Volume = {22},
      Number = {3},
  Pages= {557–573},
      Month = {March},
      Year = {2015}
  }
• M. Döhler, L. Marin, D. Bernal, L. Mevel. Statistical Decision Making for Damage Localization with Stochastic Load Vectors. Mechanical Systems and Signal Processing , 2013. (Abstract | Bibtex )
  Mechanical systems under vibration excitation are prime candidate for being modeled by linear time invariant systems. Damage detection in such systems relates to the monitoring of the changes in the eigenstructure of the corresponding linear system, and thus reflects changes in modal parameters (frequencies, damping, mode shapes) and finally in the finite element model of the structure. Damage localization using both finite element information and modal parameters estimated from ambient vibration data collected from sensors is possible by the Stochastic Dynamic Damage Location Vector (SDDLV) approach. Damage is related to some residual derived from the kernel of the difference between transfer matrices in both reference and damage states and a model of the reference state. Deciding that this residual is zero is up to now done using an empirically defined threshold. In this paper, we show how the uncertainty in the estimates of the state space system can be used to derive uncertainty bounds on the damage localization residuals to decide about the damage location with a hypothesis test.
  @article{mssp13-3,
      Author = {Döhler, Michael and Marin, Luciano and Bernal, Dionisio, and Mevel, Laurent},
      Title = {Statistical Decision Making for Damage Localization with Stochastic Load Vectors},
      Journal = {Mechanical Systems and Signal Processing},
      Volume = {39},
      Number = {1},
      Month = {426-440},
      Year = {2013}
  }
• R. Zouari, L. Mevel, M. Basseville. An Adaptive Statistical Approach To Flutter Detection . AIAA Journal of Aircraft , 2012. (Abstract | Bibtex )
  One important issue to be handled online during flight testing is flutter monitoring, here addressed as a detection problem. From subspace detection algorithms proposed for vibration-based monitoring, several online flutter monitoring algorithms have been designed by the authors. They are based on a recursive version of the subspace based residual and on an hypothesis test for detecting changes in a specific instability indicator with respect to a fixed reference modal parameter (identified on a safe structure). However the flutter onset time provided by those algorithms turns out to be too conservative. In this paper, a moving reference approach is proposed to overcome that issue. Two adaptive flutter monitoring algorithms are proposed that update the reference modal state during the online test. The usefulness of the proposed approach is discussed based on experimental results obtained on simulation data provided by two academic and industry relevant simulated aircraft models.
  @article{AIAA-flutter,
  Author = {Zouari, Rafik and Mevel, Laurent and Basseville, Mich\`ele},
  Title = {An Adaptive Statistical Approach To Flutter Detection},
  Journal = {AIAA Journal of Aircraft},
  Volume = {49},
  Number = {3},
  Month = {May},
  Year = {2012}
  }

Identification and subspace methods

• M, Döhler, L. Mevel. Fast Multi-Order Stochastic Subspace Identification . In 18th IFAC World Congress , Milan, Italy, August 2011. (Abstract | Bibtex )
  Stochastic subspace identification methods are an efficient tool for system identi- fication of mechanical systems in Operational Modal Analysis (OMA), where modal parameters are estimated from measured vibrational data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown and identification results at different model orders need to be compared to distinguish true structural modes from spurious modes in so-called stabilization diagrams. In this paper, this multi-order system identification with the subspace-based identification algorithms is studied and an efficient algorithm to estimate the system matrices at multiple model orders is derived.
  @InProceedings{ifac111,
  Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Fast Multi-Order Stochastic Subspace Identification},
  BookTitle = {Proceedings of the 18th IFAC World Congress},
  Pages={0-0},
  Address = {Milan, Italy},
  Month = {August},
  Year = {2011}
  }

Damage Localization

• M, Döhler, L. Mevel. Efficient Computation of Minmax Tests for Fault Isolation and Their Application to Structural Damage Localization . In 19th IFAC World Congress , Cape Town, south Africa, August 2014. (Abstract | Bibtex )
  For applications as Operational Modal Analysis (OMA) of vibrating structures, an output-only LTI system with state and measurement noise can be identified using subspace methods. While these identification techniques have been very suitable for the identification of such mechanical, aeronautical or civil structures, covariance expressions of the estimates of the system matrices are difficult to obtain and theoretical results from literature are hard to implement for output-only systems with unknown noise properties in practice. Moreover, the model order of the underlying system is generally unknown and due to noise and model errors, usual statistical criteria cannot be used. Instead, the system is estimated at multiple model orders and some GUI driven stabilization diagram containing the resulting modal parameters is used by the structural engineer. Then, the covariance of the estimates at these different model orders is an important information for the engineer, which, however, would be computationally expensive to obtain with the existing tools. Recently a fast multi-order version of the stochastic subspace identification approach has been proposed, which is based on the use of the QR decomposition of the observability matrix at the largest model order. In this paper, the corresponding covariance expressions for the system matrix estimates at multiple model orders are derived and successfully applied on real vibration data.
  @InProceedings{ifac112,
  Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Efficient Computation of Minmax Tests for Fault Isolation and Their Application to Structural Damage Localization},
  BookTitle = {Proceedings of the 19th IFAC World Congress},
  Pages={0-0},
  Address = {Cape Town, South Africa},
  Month = {August},
  Year = {2014}
  }
• M, Döhler, L. Mevel, D. Bernal. Statistical Subspace-Based Model-Free Damage Localization . In Engineering Mechanics Institute Conference, Boston, USA, June 2011. (Abstract | Bibtex )
  Statistical methods using output-only data have been shown to offer a robust solution to the damage detection task. These techniques have also been combined with sensitivities extracted from finite element models to offer information on the location of damage accounting for uncertainties in the finite element sensitivities. In some applications, however, the formulation of the finite element model makes implementation impractical and this motivates the search for model-free damage localization alternatives. One option is to use experimentally extracted sensitivities but their computation requires a set of constants (usually absorbed in the normalization of the eigenvectors) that are not available in output only identification. The noted limitation can be circumvented by adding a known perturbation to the mass distribution and repeating the output only identification, a procedure that can be practical in some cases. Linking a null-space based subspace damage index with experimentally extracted sensitivities allows us to infer on the position of damage without formulating a finite element model and without the need for input measurements. Special care was taken of the robustness of the test with respect to false localization alarms. An orthogonal projection approach is used in order to increase the sharpness of the discrimination between safe and damaged elements. The performance of the algorithm is illustrated on simulated data.
  @InProceedings{emi11,
  Author = {Döhler, Michael and Mevel, Laurent and Bernal, Dionisio},
      Title = {Statistical Subspace-Based Model-Free Damage Localization},
  BookTitle = {Proceedings of the Engineering Mechanics Institute Conference (EMI2011)},
  Pages={0-0},
  Address = {Boston, USA},
  Month = {June},
  Year = {2011}
  }
• L. Marin, M, Döhler, L. Mevel, D. Bernal. Statistical based decision making for damage localization with influence lines . In International Workshop on Structural Health Monitoring, Stanford, USA, September 2013. (Abstract | Bibtex )
  A theorem on damage localization from flexibility changes has been proven re-cently, where it has been shown that the image of the change in flexibility δF between damaged and reference states of a structure is a basis for the influence lines of stress resultants at the damaged locations. This damage localization approach can operate on output-only vibration measurements from damaged and reference states, and a finite element model of the structure in reference state is required. While the localization approach is based on purely mechanical principles, an estimate of the image of δF is required from the data that is subject to statistical uncertainty due to unknown noise excitation and finite data length. In this paper, this uncertainty is quantified from the measurements and a statistical framework is added for the decision about damaged elements. The combined approach is successfully applied to a numerical simulation and to a cantilever beam in a lab experiment.
  @InProceedings{marin-iwshm13,
  Author = {Marin, Luciano and Döhler, Michael and Bernal, Dionisio and Mevel, Laurent},
      Title = {Statistical based decision making for damage localization with influence lines},
  BookTitle = {Proceedings of the 9th International Workshop on Structural Health Monitoring},
  Pages={0-0},
  Address = {Stanford, USA},
  Month = {September},
  Year = {2013}
  }

Damage detection

• A. Ashari, L. Mevel. Input-Output Subspace-Based Fault Detection . In SAFEPROCESS Conference, Mexico City, Mexico, August 2012. (Abstract | Bibtex )
  Subspace-based fault detection method using input-output information is developed in this paper. In some practical applications, the environment noise is the only input that excites the system. Although the statistical properties of the noise might be estimated, the value of the noise is not usually available at each time instance. The traditional subspace fault detection is already developed for such situations. In several other applications, measured inputs are applied to the system or even the stochastic noise might be measurable. While it is still possible to use the traditional output-only detection method, it is reasonable to expect that the application of extra input information together with the output data improves the detection. Several computation issues are discussed in this paper to include input data in the detection method, correctly. Simulation results show the efficiency of using the input information to improve the quality of fault detection.
  @InProceedings{safe122,
  Author = {Ashari, Alireza and Mevel, Laurent},
      Title = {Input-Output Subspace-Based Fault Detection},
  BookTitle = {Proceedings of the 8th IFAC Conference on Decision and Control and European Control Conference},
  Pages={0-0},
  Address = {Mexico City, Mxico},
  Month = {August},
  Year = {2012}
  }
• A. Ashari, L. Mevel. Input design for subspace-based fault detection. In 50th IEEE Conference on Decision and Control and European Control Conference, Milan, Italy, August 2011. (Abstract | Bibtex )
  This paper considers the problem of auxiliary input design for subspace-based fault detection methods. In several real applications, particularly in the damage detection of mechanical structures and vibrating systems, environment noise is the only input to the system. In some applications, white noise produces low quality output data for the subspace-based fault detection method. In those methods, a residual is calculated to detect the fault based on the output information. However, some modes of the system may not influence the outputs and the residual appropriately if the input is not exciting enough for those modes. In this paper, “rotated inputs” method is implemented to excite the system modes. In addition to produce a residual more sensitive to the weak modes, it is possible to detect system order changes due to the fault using the rotated inputs. Simulation results demonstrate the efficiency of injecting these auxiliary inputs to improve the subspace-based fault detection methodology.
  @InProceedings{cdc112,
  Author = {Ashari, Alireza and Mevel, Laurent},
      Title = {Input design for subspace-based fault detection},
  BookTitle = {Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference},
  Pages={0-0},
  Address = {Orlando, USA},
  Month = {December},
  Year = {2011}
  }
• M, Döhler, L. Mevel. Robust Subspace Based Fault Detection . In 18th IFAC World Congress , Milan, Italy, August 2011. (Abstract | Bibtex )
  Subspace methods enjoy some popularity, especially in mechanical engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, some subspace based fault detection residual has been recently proposed and applied successfully. However, most works assume that the unmeasured ambient excitation level during measurements of the structure in the reference and possibly damaged condition stays constant, which is not satised by any application. This paper addresses the problem of robustness of such fault detection methods. An ecient subspace-based fault detection test is derived that is robust to excitation change but also to numerical instabilities that could arise easily in the computations. Furthermore, the fault detection test is extended to the Unweighted Principal Component subspace algorithm.
  @InProceedings{ifac112,
  Author = {Döhler, Michael and Mevel, Laurent},
      Title = {Robust Subspace Based Fault Detection},
  BookTitle = {Proceedings of the 18th IFAC World Congress},
  Pages={0-0},
  Address = {Milan, Italy},
  Month = {August},
  Year = {2011}
  }
• F. Hille, M, Döhler, L. Mevel, W. Rucker. Subspace based damage detection methods on a prestressed concrete bridge . In 8th Eurodyn conference , Leuven, Belgium, July 2011. (Abstract | Bibtex )
  For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Methodologies from control engineering especially of aerospace applications have been adopted and converted for the application on civil structures. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. A null space based damage detection algorithm is tested for its sensitivity to structural damage of a prestressed concrete road bridge. Specific techniques and extensions of the algorithm are used to overcome difficulties from the size of the structure which is associated with the number of recorded sensor channels as well as from the operational disturbances by a nearby construction site. It can be shown that for concrete bridges the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system. Small damages which do not result in a system change when not activated by loading, do not lead to a modification of the dynamic response behavior and for that cannot be detected with the proposed global monitoring method
  @InProceedings{eurodyn11,
  Author = {Hille, Falk and Döhler, Michael and Mevel, Laurent and Rucker, Werner},
      Title = {Subspace based damage detection methods on a prestressed concrete bridge},
  BookTitle = {Proceedings of the Eighth International Conference on Structural Dynamics EURODYN},
  Pages={0-0},
  Address = {Milan, Italy},
  Month = {August},
  Year = {2011}
  }
• F. Hille, M, Döhler, L. Mevel, W. Rucker. Structural Health Monitoring during Progressive Damage Test of S101 Bridge. In 8th International Workshop on Structural Health Monitoring, Stanford, USA, September 2011. (Abstract | Bibtex )
  For the last decades vibration based identification of damage on civil engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in that field has been rapidly expanding from classic modal parameter estimation using measured excitation to modern operational monitoring. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. In this paper, two methods accounting for statistical and/or operational uncertainties are applied to measurement data of a progressive damage test on a prestressed concrete bridge. On the base of covariance driven Stochastic Subspace Identification (SSI) an algorithm is developed to monitor and automatically compute confidence intervals of the obtained modal parameters. Furthermore, a null space based non-parametric damage detection method, utilizing a statis- tical χ2 type test is applied to the measurement data. It can be shown that for concrete bridges the proposed methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
  @InProceedings{stanford11,
  Author = {Hille, Falk and Döhler, Michael and Mevel, Laurent and Rucker, Werner},
      Title = {Structural Health Monitoring during Progressive Damage Test of S101 Bridge},
  BookTitle = {Proceedings of the 8th International Workshop on Structural Health Monitoring},
  Pages={0-0},
  Address = {Stanford, CA, USA},
  Month = {September},
  Year = {2011}
  }

Uncertainty quantification

• M, Döhler, X-B. Lam, L. Mevel. Multi-Order Covariance Computation for Estimates in Stochastic Subspace Identification Using QR Decompositions. . In 19th IFAC World Congress , Cape Town, South Africa, August 2014. (Abstract | Bibtex )
  For applications as Operational Modal Analysis (OMA) of vibrating structures, an output-only LTI system with state and measurement noise can be identified using subspace methods. While these identification techniques have been very suitable for the identification of such mechanical, aeronautical or civil structures, covariance expressions of the estimates of the system matrices are difficult to obtain and theoretical results from literature are hard to implement for output-only systems with unknown noise properties in practice. Moreover, the model order of the underlying system is generally unknown and due to noise and model errors, usual statistical criteria cannot be used. Instead, the system is estimated at multiple model orders and some GUI driven stabilization diagram containing the resulting modal parameters is used by the structural engineer. Then, the covariance of the estimates at these different model orders is an important information for the engineer, which, however, would be computationally expensive to obtain with the existing tools. Recently a fast multi-order version of the stochastic subspace identification approach has been proposed, which is based on the use of the QR decomposition of the observability matrix at the largest model order. In this paper, the corresponding covariance expressions for the system matrix estimates at multiple model orders are derived and successfully applied on real vibration data.
  @InProceedings{ifac112,
  Author = {Döhler, Michael and LAm, Xuan-Binh and Mevel, Laurent},
      Title = {Multi-Order Covariance Computation for Estimates in Stochastic Subspace Identification Using QR Decompositions},
  BookTitle = {Proceedings of the 19th IFAC World Congress},
  Pages={0-0},
  Address = {Cape Town, south Africa},
  Month = {August},
  Year = {2014}
  }
• L. Marin, M, Döhler, D. Bernal, L. Mevel. Uncertainty Quantification for Stochastic Damage Localization for Mechanical Systems. In SAFEPROCESS Conference, Mexico City, Mexico, August 2012. (Abstract | Bibtex )
  Mechanical systems under vibration excitation are prime candidate for being modeled by linear time invariant systems. Damage detection in such systems relates to the monitoring of the changes in the eigenstructure of the corresponding linear system, and thus reflects changes in modal parameters (frequencies, damping, mode shapes) and finally in the finite element model of the structure. Damage localization using both finite element information and modal parameters estimated from ambient vibration data collected from sensors is possible by the Stochastic Dynamic Damage Location Vector (SDDLV) approach. Damage is related to some residual derived from the kernel of the difference between transfer matrices in both reference and damage states and a model of the reference state. Deciding that this residual is zero is up to now done using some empirically defined threshold. In this paper, we show how the derivation of the uncertainty of the state space system can be used to derive uncertainty on the damage localization residuals and help to decide about the damage location.
  @InProceedings{safe121,
  Author = {Marin, Luciano and Döhler, Michael and Bernal, Dionisio and Mevel, Laurent},
      Title = {Uncertainty Quantification for Stochastic Damage Localization for Mechanical Systems},
  BookTitle = {Proceedings of the 8th IFAC Conference on Decision and Control and European Control Conference},
  Pages={0-0},
  Address = {Mexico City, Mxico},
  Month = {August},
  Year = {2012}
  }
• M, Döhler, X-B. Lam, L. Mevel. Uncertainty Quantification for Stochastic Subspace Identification on Multi-Setup Measurements . In 50th IEEE Conference on Decision and Control and European Control Conference, Milan, Italy, August 2011. (Abstract | Bibtex )
  In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained from Stochastic System Identification of structures, are subject to statistical uncertainty from ambient vibration measurements. It is hence necessary to evaluate the uncertainty bounds of these obtained results. To obtain vibration measurements at many coordinates of a structure with only a few sensors, it is common practice to use multiple sensor setups for the measurements. Recently, a multi-setup subspace identification algorithm has been proposed that merges the data from different setups first to obtain one set of global modal parameters. This paper proposes an algorithm that efficiently estimates the uncertainty on modal parameters obtained from this multi-setup subspace identification.
  @InProceedings{cdc111,
  Author = {Döhler, Michael and Lam, Xuan-Binh and Mevel, Laurent},
      Title = {Uncertainty Quantification for Stochastic Subspace Identification on Multi-Setup Measurements},
  BookTitle = {Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference},
  Pages={0-0},
  Address = {Orlando, USA},
  Month = {December},
  Year = {2011}
  }
• X-B. Lam, L. Mevel. Uncertainty Quantification for Eigensystem-Realization-Algorithm, A Class of Subspace System Identification. In 18th IFAC World Congress , Milan, Italy, August 2011. (Abstract | Bibtex )
  In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained from Stochastic Subspace Identification of structures, are subject to statistical uncertainty from ambient vibration measurements. It is hence neccessary to evaluate the confidence intervals of these obtained results. This paper will propose an algorithm that can efficiently estimate the uncertainty on modal parameters obtained from the Eigensystem-Realization-Algorithm (ERA). The algorithm is validated on a relevant industrial example.
  @InProceedings{ifac113,
  Author = {Lam, Xuan-Binh and Mevel, Laurent},
      Title = {Uncertainty Quantification for Eigensystem-Realization-Algorithm, A Class of Subspace System Identification},
  BookTitle = {Proceedings of the 18th IFAC World Congress},
  Pages={0-0},
  Address = {Milan, Italy},
  Month = {August},
  Year = {2011}
  }

Rotating systems

Ground Resonance phenomena

• A. Jhinaoui, L. Mevel, J. Morlier . Uncertainties Quantification for Subspace Identification of rotating Systems . In 5th IFAC International Workshop on Periodic Control Systems (PSYCO'2013) , Caen, France, July 2013. (Abstract | Bibtex )
  The dynamics of rotating systems such as helicopters and wind turbines show periodically time-varying behavior. Dierently from the linear time-invariant case, such systems cannot be characterized by the classical modal parametrization. An alternative description is made possible with the Floquet theory which extends the modal analysis and the notions of frequencies and damping ratios to the periodic case. Based on this description, the present paper suggests a new output-only subspace identication algorithm able to extract the modal parameters. Besides, in operational modal analysis, the identied modes may be subjected to statistical uncertainty from ambient vibration measurements. Hence, the suggested identication algorithm will allow as well an ecient estimation of the uncertainty bounds on the identied modal parameters.
  @InProceedings{psyco13,
  Author = Jhinaoui, Ahmed and Mevel, Laurent and Morlier, Joseph},
  Title = {Uncertainties Quantification for Subspace Identification of rotating Systems},
  BookTitle = {Proceedings of the 5th IFAC International Workshop on Periodic Control Systems (PSYCO'2013)},
  Pages={0-0},
  Address = {Caen, France},
  Month = {July},
  Year = {2013}
  }
• A. Jhinaoui, L. Mevel, J. Morlier . Subspace Instability monitoring for Linear Periodically Time-varying Systems . In 16th IFAC Symposium on System Identification (SYSID 2012) , Bruxelles, Belgium, July 2012. (Abstract | Bibtex )
  Most subspace-based methods enabling instability monitoring are restricted to the linear time-invariant (LTI) systems. In this paper, a new subspace method of instability monitoring is proposed for the linear periodically time-varying (LPTV) case. For some LPTV systems, the system transition matrices may depend on some parameter and are also periodic in time. A certain range of values for the parameter leads to an unstable transition matrix. Early warning should be given when the system gets close to that region, taking into account the time variation of the system. Using the theory of Floquet, some symptom parameter of stability- or residual- is de ned. Then, the parameter variation is tracked by performing a set of parallel cumulative sum (CUSUM) tests. Finally, the method is tested on a simulated model of a helicopter with hinged blades, for monitoring the ground resonance phenomenon.
  @InProceedings{sysid121,
  Author = Jhinaoui, Ahmed and Mevel, Laurent and Morlier, Joseph},
  Title = {Subspace Instability monitoring for Linear Periodically Time-varying Systems },
  BookTitle = {Proceedings of the 16th IFAC Symposium on System Identification (SYSID 2012) },
  Pages={0-0},
  Address = {Bruxelles, Belgium},
  Month = {July},
  Year = {2012}
  }
• A. Jhinaoui, L. Mevel, J. Morlier . Subspace Identification for Linear Periodically Time-varying Systems. In 16th IFAC Symposium on System Identification (SYSID 2012) , Bruxelles, Belgium, July 2012. (Abstract | Bibtex )
  In this paper, an extension of the output-only subspace identification, to the class of linear periodically time-varying (LPTV) systems, is proposed. The goal is to identify a useful information about the system's stability using the Floquet theory which gives a necessary and sucient condition for stability analysis. This information is retrieved from a matrix called the monodromy matrix, which is extracted by some simultaneous singular value decompositions (SVD) and from a resolution of a least squares criterion. The method is, finally, illustrated by a simulation of a model of a helicopter with a hinged-blades rotor. .
  @InProceedings{sysid122,
  Author = Jhinaoui, Ahmed and Mevel, Laurent and Morlier, Joseph},
  Title = {Subspace Identification for Linear Periodically Time-varying Systems},
  BookTitle = {Proceedings of the 16th IFAC Symposium on System Identification (SYSID 2012) },
  Pages={0-0},
  Address = {Bruxelles, Belgium},
  Month = {July},
  Year = {2012}
  }

Wind turbines

• A. Jhinaoui, L. Mevel, J. Morlier . Generalized subspace identification for rotating systems: application to a wind turbine. In Leuven Conference on Noise and Vibration Engineering (ISMA 2012) , Bruxelles, Belgium, July 2012. (Abstract | Bibtex )
  In this paper, an extension of the output-only subspace identification, to the class of linear periodically time-varying (LPTV) systems, is proposed. The goal is to identify a useful information about the system's stability using the Floquet theory which gives a necessary and sucient condition for stability analysis. This information is retrieved from a matrix called the monodromy matrix, which is extracted by some simultaneous singular value decompositions (SVD) and from a resolution of a least squares criterion. The method is, finally, illustrated by data from a wind turbine.
  @InProceedings{isma-12,
  Author = Jhinaoui, Ahmed and Mevel, Laurent and Morlier, Joseph},
  Title = {Generalized subspace identification for rotating systems: application to a wind turbine},
  BookTitle = {Proceedings of the Leuven Conference on Noise and Vibration Engineering (ISMA 2012)},
  Pages={0-0},
  Address = {Leuven, Belgium},
  Month = {September},
  Year = {2012}
  }
• A. Jhinaoui, L. Mevel, J. Morlier . Vibration monitoring of operational wind turbine. In Proc. 9th International Workshop on Structural Health Monitoring , Stanford, CA, September 2013. (Abstract | Bibtex )
  The modal analysis of a wind turbine has been generally handled with the assumption that this structure can be accurately modeled as linear time-invariant. Such assumption may be misleading for stability analysis, especially, with the current development of large and very large wind turbines for which the effect of gravity and aerodynamics are very. And therefore, the inherent periodically time-varying dynamics of wind turbines (and for rotating systems, in general) should be taken into account. Based on the Floquet approach, this paper suggests an on-line vibration monitoring algorithm able to detect any trend of a given wind turbine toward an unstable behavior, before any dysfunction or destruction occurs. This turns out to the detection of any change on some residuals using a bank of cumulative sum tests.
  @InProceedings{iwshm-13,
  Author = Jhinaoui, Ahmed and Mevel, Laurent and Morlier, Joseph},
  Title = {Vibration monitoring of operational wind turbine},
  BookTitle = {Proc. 9th International Workshop on Structural Health Monitoring},
  Pages={0-0},
  Address = {Stanford, USA},
  Month = {September},
  Year = {2013}
  }

Flutter

Frequency domain methods

• P. Mellinger, M. Döhler, L. Mevel, G. Broux. Data Fusion for Frequency Domain Stochastic Subspace Identification. In 5th European Conference on Structural Control, Genoa, Italy, June 2011. (Abstract | Bibtex )
  In Operational Modal Analysis (OMA) of large structures it is often needed to process output-only sensor data from multiple non-simultaneously recorded measurement setups, where some reference sensors stay fixed, while the others are moved between the setups. A standard approach to process the data together for global system identification is to transfer the data into frequency domain and merge it there. However, this only works if the unmeasured ambient excitation remains stationary throughout all setups. As the ambient excitation can be different from setup to setup, the amplitude of the measured data can be different as well and the data has to be normalized. Recently, a method has been developed for covariance- and data-driven Stochastic Subspace Identification (SSI) to automatically normalize and merge the data from multiple setups in order to obtain the global modal parameters (natural frequencies, damping ratios, mode shapes), instead of doing the SSI for each setup separately. In this paper, we adapt this approach to multi-setup SSI in frequency domain, where we use spectra data instead of time series data. We demonstrate the advantages of the new merging approach in the frequency domain and apply it to a relevant industrial large scale example, where we compare the estimation results of the modal parameters between the time and frequency domain approaches.
  @InProceedings{eacs121,
  Author = {Mellinger, Philippe and Döhler, Michael and Mevel, Laurent and Broux, Gabriel},
      Title = {Data Fusion for Frequency Domain Stochastic Subspace Identification},
  BookTitle = {Proceedings of the 5th Conference on Structural Control (EACS 2012)},
  Pages={0-0},
  Address = {Genoa, Italy},
  Month = {June},
  Year = {2012}
  }