System Identification of Constructed Facilities: Challenges and Opportunities Across Hazards

The motivation, success and prevalence of full-scale monitoring of constructed buildings vary considerably across the hazard of concern (earthquakes, strong winds, etc.), due in part to various fiscal and life safety motivators. Yet while the challenges of successful deployment and operation of large-scale monitoring initiatives are significant, they are perhaps dwarfed by the challenges of data management, interrogation and ultimately system identification. Practical constraints on everything from sensor density to the availability of measured input has driven the development of a wide array of system identification and damage detection techniques, which in many cases become hazard-specific. In this study, the authors share their experiences in fullscale monitoring of buildings across hazards and the associated challenges of system identification. The study will conclude with a brief agenda for next generation research in the area of system identification of constructed facilities

Frequency-domain subspace identification of nonlinear mechanical systems - Application to a solar array structure

The present paper addresses the experimental identification of a simplified realisation of a solar array structure in folded configuration. To this end, a nonlinear subspace identification technique formulated in the frequency domain, referred to as the FNSI method, is exploited. The frequency response functions of the underlying linear structure and the nonlinear coefficients are estimated by this approach. Nonlinearity is caused by impacts between adjacent panels and friction and gaps appearing in their clamping interfaces. This application is challenging for several reasons, which include high modal density and the complicated nature of the involved nonlinear mechanisms

Sleep Analytics and Online Selective Anomaly Detection

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science. Scientists have segmented sleep into several stages and stage two is characterized by two patterns (or anomalies) in the EEG time series recorded on sleep subjects. These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was introduced to design a residual system, where all anomalies (known and unknown) are detected but the system only triggers an alarm when non-SS anomalies appear. The solution of the OSAD problem required us to combine techniques from both machine learning and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.Comment: Submitted to 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Dual Mode MPC for a Concentrated Solar Thermal Power Plant

A model predictive control strategy for a concentrated solar thermal power plant is proposed. Design of the proposed controller is based on an estimated linear time-invariant state space model around a nominal operating point. The model is estimated directly from input-output data using the subspace identification method and taking into account the frequency response of the plant. Input-output data are obtained from a nonlinear distributed parameter model of a plant rather than the plant itself. Effectiveness of the proposed control strategy in terms of tracking and disturbance rejection is evaluated through two different scenarios created in a nonlinear simulation environment

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used

Closed and Open Loop Subspace System Identification of the Kalman Filter

Some methods for consistent closed loop subspace system identification presented in the literature are analyzed and compared to a recently published subspace algorithm for both open as well as for closed loop data, the DSR_e algorithm. Some new variants of this algorithm are presented and discussed. Simulation experiments are included in order to illustrate if the algorithms are variance efficient or not

Ambient vibration re-testing and operational modal analysis of the Humber Bridge

An ambient vibration survey of the Humber Bridge was carried out in July 2008 by a combined team from the UK, Portugal and Hong Kong. The exercise had several purposes that included the evaluation of the current technology for instrumentation and system identification and the generation of an experimental dataset of modal properties to be used for validation and updating of finite element models for scenario simulation and structural health monitoring. The exercise was conducted as part of a project aimed at developing online diagnosis capabilities for three landmark European suspension bridges. Ten stand-alone tri-axial acceleration recorders were deployed at locations along all three spans and in all four pylons during five days of consecutive one-hour recordings. Time series segments from the recorders were merged, and several operational modal analysis techniques were used to analyse these data and assemble modal models representing the global behaviour of the bridge in all three dimensions for all components of the structure. The paper describes the equipment and procedures used for the exercise, compares the operational modal analysis (OMA) technology used for system identification and presents modal parameters for key vibration modes of the complete structure. The results obtained using three techniques, natural excitation technique/eigensystem realisation algorithm, stochastic subspace identification and poly-Least Squares Frequency Domain method, are compared among themselves and with those obtained from a 1985 test of the bridge, showing few significant modal parameter changes over 23 years in cases where direct comparison is possible. The measurement system and the much more sophisticated OMA technology used in the present test show clear advantages necessary due to the compressed timescales compared to the earlier exercise. Even so, the parameter estimates exhibit significant variability between different methods and variations of the same method, while also varying in time and having inherent variability. (C) 2010 Elsevier Ltd. All rights reserved

Wigner's theorem on Grassmann spaces

Wigner's celebrated theorem, which is particularly important in the mathematical foundations of quantum mechanics, states that every bijective transformation on the set of all rank-one projections of a complex Hilbert space which preserves the transition probability is induced by a unitary or an antiunitary operator. This vital theorem has been generalised in various ways by several scientists. In 2001, Molnár provided a natural generalisation, namely, he provided a characterisation of (not necessarily bijective) maps which act on the Grassmann space of all rank-n projections and leave the system of Jordan principal angles invariant (see [17] and [20]). In this paper we give a very natural joint generalisation of Wigner's and Molnár's theorems, namely, we prove a characterisation of all (not necessarily bijective) transformations on the Grassmann space which fix the quantity TrPQ (i.e. the sum of the squares of cosines of principal angles) for every pair of rank-n projections P and Q

A time-varying inertia pendulum: Analytical modelling and experimental identification

In this paper two of the main sources of non-stationary dynamics, namely the time-variability and the presence of nonlinearity, are analysed through the analytical and experimental study of a time-varying inertia pendulum. The pendulum undergoes large swinging amplitudes, so that its equation of motion is definitely nonlinear, and hence becomes a nonlinear time-varying system. The analysis is carried out through two subspace-based techniques for the identification of both the linear time-varying system and the nonlinear system. The flexural and the nonlinear swinging motions of the pendulum are uncoupled and are considered separately: for each of them an analytical model is built for comparisons and the identification procedures are developed. The results demonstrate that a good agreement between the predicted and the identified frequencies can be achieved, for both the considered motions. In particular, the estimates of the swinging frequency are very accurate for the entire domain of possible configurations, in terms of swinging amplitude and mass positio