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

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless- or dissipative system from a given noise-free trajectory; important special cases are passive- and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; state-space equations are then computed solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order mode

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

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