System identification based structural damage aggravation detection in a large masonry building

Summary form only given. In this paper, the authors use the numerical algorithm for subspace state space system identification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective even when the probing signal is small.

This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static nonlinearities follow from a low-rank approximation of a matrix obtained from this regression problem.

A novel nonlinear modelling approach has been developed and implemented on Alstom gasifier using Wiener model. The linear element of the Wiener model was identified by a combined subspace state space method, which integrated MOESP (Multivariable Output-Error State Space) and N4SID (Numerical algorithms for subspace state space system identification) method in the estimation of system matrices. Then a single layer neural network was chosen as the nonlinearity of the model. The proposed model identification method was used to model Alstom gasifier with strong nonlinearity and multivariable couples. The results compared to a combined linear subspace identification method demonstrate that the nonlinear method proposed in this paper behave better approximation.

The performance of machines and equipment degrades as a result of aging and wear. This decreases performance reliability and increases the potential for faults and failures. To ensure proper functionality of complex systems, advanced technologies for performance diagnosis and control are being incorporated into engineering designs, which requires an ever-increasing number of sensors and measurement devices. Nevertheless, a sensor, just as any other dynamic system, degrades and fails. A faulty sensor may cause process performance degradation, process shut down, or even a fatal accident because it is no longer able to deliver accurate information about the monitored system. Therefore, it is essential to assess sensor performance to ensure system reliability. In this paper, a method is proposed to detect, isolate, and compensate sensor degradation. The numerical algorithm for subspace state space system identification is used to track the changes of the time constants and gains of the sensor and the monitored system. Without imposing requirements for redundant sensors and measurement devices, this method utilizes the fact that sensor readings depict dynamic characteristics of the sensors as well as those of the monitored system. The newly proposed method is verified in angular sensor degradation detection using high-fidelity simulations of an automotive electronic throttle system.

In this paper, the inverted pendulum is controlled by Quanser universal benchtop equipment-direct current (QUBE DC) motor, and the motor inverted pendulum system is identified when the sinusoidal signal is input. However, there is a large error when the angle of the pendulum is pre-identified by numerical algorithms for subspace state space system identification algorithm. Therefore, this paper designs a multi strategy optimization simulated annealing particle swarm optimization algorithm, which can accurately identify the complex system with sinusoidal input signal. In the identification experiments of sinusoidal signals with multiple frequencies and amplitudes, this paper found that the algorithm performs relatively best at a frequency of 8 rad/s. Moreover, at a frequency of 8 rad/s, the algorithm can quickly reduce the error to 1.69% within 100 generations. Finally, based on the hardware model identified by particle swarm optimization, this paper designs an explicit model predictive control method to control the inverted pendulum, and tests the constraint processing and anti-disturbance performance of the system under the voltage pulse interference with different duty cycles, which realizes the inverted pendulum balance and has robustness under the interference.

In this paper the performance of four system identification methods is evaluated for the analysis of different power system configurations. The methods considered are: nonlinear least squares (NLS), Prony method, sub-space state space system identification (N4SID) and the prediction error method (PEM). Artificially created data distorted by noise are used to represent real-world conditions. The analysis verifies the practical value of system identification methods for power system dynamic analysis and also illustrates practical issues and solutions encountered in their application.

This paper addresses identification of Hammerstein systems using the sampled input-output data in frequency domain. When the static memoryless nonlinear part of Hammerstein model can be considered as a polynomial with a known order, the following linear part can be estimated as a multi-input single-output (MISO) systems with the numerical algorithm for subspace state space system identification method. Simulation results illustrate this approach is effective.

Damage detection and structural health monitoring have always been of great importance to civil engineers and researchers. Vibration-based damage detection has several advantages compared to traditional methods of non-destructive evaluation, such as ground penetrating radar (GPR) or ultrasonic testing, since they give a global response and are feasible for large structures. Damage detection requires a comparison between two systems states, the baseline or “healthy state”, i.e., the initial modal parameters, and the damaged state. In this study, system identification (SI) was carried out on a pedestrian bridge by measuring the dynamic response using six low-cost triaxial accelerometers. These low-cost accelerometers use a micro-electro-mechanical system (MEMS), which is cheaper compared to a piezoelectric sensor. The frequency domain decomposition algorithm, which is an output-only method of modal analysis, was used to obtain the modal properties, i.e., natural frequencies and mode shapes. Three mode shapes and frequencies were found out using system identification and were compared with the finite element model (FEM) of the bridge, developed using the commercial finite element software, Abaqus. A good comparison was found between the FEM and SI results. The frequency difference was nearly 10%, and the modal assurance criterion (MAC) of experimental and analytical mode shapes was greater than 0.80, which proved to be a good comparison despite the small number of accelerometers available and the simplifications and idealizations in FEM.

Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four‐tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First‐Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude‐modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

This paper presents the system identification technique by applying PZT actuators and PVDF sensors. An array of finite-length PVDF films is equally spaced and distributed over a cantilever beans acting as sensors. Two finite-length PZT patches are bonded symmetrically and excited 180°out-of-phase for pure bending excitation. The theoretical base of modal analysis for system identification of piezoceramic beams is presented to derive the frequency response functions (FRFs) in conventional modal format. The physical interpretation of P11 and PVDF mode shape functions are characterized respectively. The FRFs between the PZT actuators and PVDF sensors are experimentally measured. By the operation of a series of FRFs and the application of modal parameter extraction method, the modal parameters. including system natural frequencies, mode shapes and modal damping ratios, can be obtained. Both theoretical and experimental approaches agree very well. The system information can be used for control applications and also be applicable to structural failure diagnosis.

The Alfred Zampa Memorial Bridge (AZMB), a newly built long-span suspension bridge, is located 32km northeast of San Francisco on interstate Highway I-80. A set of dynamic field tests were conducted on the AZMB in November 2003, just before the bridge opening to traffic. These tests provided a unique opportunity to identify the modal properties of the bridge in its as-built condition with no previous traffic loads or seismic excitation. A benchmark study on modal identification of the AZMB is performed using three different state-of-the-art system identification algorithms based on ambient, as well as forced vibration measurements. These system identification methods consist of: (1) the multiple-reference natural excitation technique combined with the eigensystem realization algorithm; (2) the data-driven stochastic subspace identification method; and (3) the enhanced frequency domain decomposition method. Overall, the modal parameters identified using these system identification methods are found to be in very good agreement for each type of tests (ambient and forced vibration tests). For most vibration modes, the natural frequencies and mode shapes identified using the two different types of test data also match very well. However, the modal damping ratios identified from forced vibration test data are, in general, higher than those estimated from ambient vibration data. The identified natural frequencies and mode shapes are finally compared with their analytical counterparts from a three-dimensional finite-element model of the AZMB. The modal properties of the AZMB presented in this paper can be used as baseline in future health monitoring studies of this bridge.

Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

In this paper, we propose a subspace state space system identification method using prior knowledge. In this method, we give an algorithm to identify the partial state which corresponds to unknown modes of the system. This scheme is effective when some system poles cannot be identified because of the measurement noise or a high gain feedback controller. The effectiveness of the proposed method is evaluated by numerical examples including the comparison with the conventional 4SID method

This study presents the design of wide‐area fractional order damping controller for low frequency inter‐area oscillations in Western Electricity Coordinating Council (WECC) system. The developed wide‐area damping control scheme is implemented in a decentralised manner by examining the entire system through decentralised identification at the level of each generator bus and FACTS devices in large‐scale system. The decentralised identification is done by applying n4sid (Subspace State Space System Identification) algorithm to the time‐domain simulated response of WECC system. The main features of the proposed approach are: (a) measurement‐based control which does not require complete model of WECC; (b) minimise the communicating signals required for damping controller and incorporate practical method of time‐delay compensator (TDC) design to reduce the effect of time‐varying delay. The performance of wide‐area control (WAC) scheme is tested on the practical WECC system and verified by non‐linear time‐domain simulation. The proposed decentralised WAC scheme is compared with local and centralised control scheme, which enhances the damping of inter‐area oscillation effectively compared to local control scheme and more reliable compared to centralised control scheme and its robustness is verified over a wide‐range of operating conditions.

Output-only Modal Analysis using Continuous-Scan Laser Doppler Vibrometry and application to a 20 kW wind turbine