System Identification Techniques Research Articles

Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains.

Enhancing the Accuracy of Vertical Ground Reaction Force Measurement During Walking Using Pressure-Measuring Insoles.

Pressure-measuring insoles can be an attractive tool for measuring ground reaction force (GRF) since they are portable and can record multiple consecutive steps. Several researchers have, however, observed that these insoles are less accurate than instrumented force platforms. To address this issue, the authors identified transfer functions that best described each insole size to enhance the measurements of the vertical component of GRF during walking. GRF data were collected from 29 participants (6/23 male/female, 24.3 ± 6.7 yrs, 70.4 ± 23.9 kg, 1.66 ± 0.11 m) using Medilogic® pressure-measuring insoles and Kistler® force platforms for three walking trials. Participants provided the institutionally approved written consent (IRB #724468). The data from both instruments were preprocessed. A subset of the data was used to train the system identification toolbox (matlab) to identify the coefficients of several candidate transfer functions for each insole size. The resulting transfer functions were compared using all available data for each insole to assess which one modified the insole data to be closer to that of the force platform. All tested transfer functions moved the vertical component of GRF closer to the corresponding force platform data. Each insole size had a specific transfer model that that yielded the best results. Using system identification techniques produced transfer functions that, when using insole data of the vertical component of GRF as input, produced output that is comparable to the corresponding measurement using an instrumented force platform.

Modelling of a Three-Body Hinge-Barge Wave Energy Device Using System Identification Techniques

In order to increase the prevalence of wave energy converters (WECs), they must provide energy at competitive prices, especially when compared with other renewable energy sources. Thus, it is imperative to develop control system technologies that are able to maximize energy extraction from waves, such that the delivered energy cost is reduced. An important part of a model-based controller is the model that it uses. System identification techniques (SITs) provide methodologies to get accurate dynamic models from input-output data. However, even though these techniques are well developed in other application areas, they are seldom used in the context of WECs. This paper proposes several strategies based on SIT to get a linear time-invariant model for a three-body hinge-barge wave energy device using experimental data. The main advantage of the model obtained with this methodology, against other methods such as linear potential theory, is that this model remains valid even for relatively large waves and WEC displacements. Other advantages of this model are its simplicity and the low computational resources that it needs. Numerical simulations are carried out to show the validation of the obtained model against recorded experimental data.

Entropy Fuzzy System Identification for the Heave Flight Dynamics of a Model-Scale Helicopter

This article studies nonlinear system identification of a small scale and flybar-free unmanned helicopter, the Trex450 chopper, built using commercial off-the-shelf components. We employ the real-time input-output data, obtained from human-controlled flight tests, operating the aircraft under severe ground effects during the vertical flight maneuvers. We highlight the efficacy of the entropy fuzzy system identification method with respect to the performance of several well-known nonlinear system identification techniques (i.e., a Takagi-Sugeno Fuzzy system, an adaptive neuro-fuzzy inference system (ANFIS), and a nonlinear autoregressive with exogenous (NARX) model) as our benchmarks. Our research confirms the benefits of the entropy fuzzy identification technique. Despite being nonlinear, the proposed fuzzy model is relatively simple, transparent, and highly accurate to represent the complex non-linear dynamic behaviors of our unmanned helicopter under severe ground effects. Another major advantage of the proposed system identification technique is its ability to avoid overfitting, an essential requirement in modeling. Overall, the fuzzy system is also capable of achieving a delicate balance between maximizing the accuracy while minimizing the complexity of the acquired model.

Jiri Tichy and his impact on the field of active noise control

Jiri Tichy was integrally involved in a number of cutting-edge research areas in acoustics, which gave him keen insights as to where meaningful contributions to the field of acoustics could be made. These insights and connections enabled him to successfully mentor numerous students in these areas by involving them in significant research and introducing them to key contacts. One such area was the field of active noise control, where he was both a mentor and friend to the author. Jiri became actively involved in active noise control in the 1980s, at the time when active control was reborn in the digital age and began to explode as a research area. During this time, the author had the privilege of working with Jiri in this field and being mentored by him. This paper will overview some of the research by the author that was either directly or indirectly influenced by Jiri in his role as both mentor and colleague. Two such research areas include the technique of passive real-time system identification, and the method of actively minimizing acoustic energy density as a means of achieving more global attenuation of the sound field.

Improved computation in terms of accuracy and speed of LTI system response with arbitrary input

Spectral analysis and system identification techniques require suitably long data sets. Linear time-invariant (LTI) systems under random input can be demanding in terms of accurate simulation of all dynamics because of calculation time. This paper details an extension of the state-space formulation of LTI systems for higher order holds, for periodic and for exponential inputs. Exact solutions of the differential state equation are implemented into loops computing only matrix-vector multiplications. The algorithmic complexity is compared to other ordinary differential equations solvers. The presented methods can be applied to arbitrary inputs. In particular, it is shown that long system responses under random input can be computed efficiently and accurately in the time and frequency domains.

Liquid-Based Battery Temperature Control of Electric Buses

Previous research identified that battery temperature control is critical to the safety, lifetime, and performance of electric vehicles. In this paper, the liquid-based battery temperature control of electric buses is investigated subject to heat transfer behavior and control strategy. Therefore, a new transient calculation method is proposed to simulate the thermal behavior of a coolant-cooled battery system. The method is based on the system identification technique and combines the advantage of low computational effort and high accuracy. In detail, four transfer functions are extracted by a thermo-hydraulic 3D simulation model comprising 12 prismatic lithium nickel manganese cobalt oxide (NMC) cells, housing, arrestors, and a cooling plate. The transfer functions describe the relationship between heat generation, cell temperature, and coolant temperature. A vehicle model calculates the power consumption of an electric bus and thus provides the input for the transient calculation. Furthermore, a cell temperature control strategy is developed with respect to the constraints of a refrigerant-based battery cooling unit. The data obtained from the simulation demonstrate the high thermal inertia of the system and suggest sufficient control of the battery temperature using a quasi-stationary cooling strategy. Thereby, the study reveals a crucial design input for battery cooling systems in terms of heat transfer behavior and control strategy.

Experimental Study of Electrical Properties of Pharmaceutical Materials by Electrical Impedance Spectroscopy

The physicochemical characterization of pharmaceutical materials is essential for drug discovery, development and evaluation, and for understanding and predicting their interaction with physiological systems. Amongst many measurement techniques for spectroscopic characterization of pharmaceutical materials, Electrical Impedance Spectroscopy (EIS) is powerful as it can be used to model the electrical properties of pure substances and compounds in correlation with specific chemical composition. In particular, the accurate measurement of specific properties of drugs is important for evaluating physiological interaction. The electrochemical modelling of compounds is usually carried out using spectral impedance data over a wide frequency range, to fit a predetermined model of an equivalent electrochemical cell. This paper presents experimental results by EIS analysis of four drug formulations (trimethoprim/sulfamethoxazole C14H18N4O3-C10H11N3O3, ambroxol C13H18Br2N2O.HCl, metamizole sodium C13H16N3NaO4S, and ranitidine C13H22N4O3S.HCl). A wide frequency range from 20 Hz to 30 MHz is used to evaluate system identification techniques using EIS data and to obtain process models. The results suggest that arrays of linear R-C models derived using system identification techniques in the frequency domain can be used to identify different compounds.

Proximity Operations and Three Degree-of-Freedom Maneuvers Using the Smartphone Video Guidance Sensor

This paper presents the first demonstration of NASA’s Smartphone Video Guidance Sensor (SVGS) as real-time position and attitude estimator for proximity and formation maneuvers. An optimal linear quadratic Gaussian controller was used, combining a linear quadratic regulator and a Kalman filter. The system was demonstrated controlling the 3-degree of freedom planar motion of the RINGS ground units (Resonant Inductive Near-field Generation Systems). A state-space model of the system’s 3-DOF motion dynamics was derived, and model parameters extracted using a system identification technique. The system’s motion control performance is experimentally demonstrated in both tracking and formation maneuvers. The results highlight the capabilities and performance of the Smartphone Video Guidance Sensor (SVGS) as a vision-based real-time position and attitude sensor for motion control, formation flight and proximity operations. A leader-follower formation maneuver approach is demonstrated, as well as position hold and path following.

An Optimal Model Identification Algorithm of Nonlinear Dynamical Systems With the Algebraic Method

Abstract This article proposes a nonparametric system identification technique to discover the governing equation of nonlinear dynamic systems with the focus on practical aspects. The algorithm builds on Brunton’s work in 2016 and combines the sparse regression with an algebraic calculus to estimate the required derivatives of the measurements. This reduces the required derivative data for the system identification. Furthermore, we make use of the concepts of K-fold cross validation from machine learning and information criteria for model selection. This allows the system identification with less measurements than the typically required data for the sparse regression. The result is an optimal model for the underlining system of the data with a minimum number of terms. The proposed nonparametric system identification method is applicable for multiple-input–multiple-output systems. Two examples are presented to demonstrate the proposed method. The first one makes use of the simulated data of a nonlinear oscillator to show the effectiveness and accuracy of the proposed method. The second example is a nonlinear rotary flexible beam. Experimental responses of the beam are used to identify the underlining model. The Coulomb friction in the servo motor together with other nonlinear terms of the system variables are found to be important components of the model. These are, otherwise, not available in the theoretical linear model of the system.

Torque measurements from MW wind turbine Gearboxes: a system identification approach

The gearbox is a critical component of modern MW wind turbines. An accurate model of the gearbox dynamics is needed to improve gearbox design, develop advanced control algorithms, and more effective fault diagnosis tools which could lead to lower the cost of energy from wind. The objective of this paper is to investigate how torque measurements can be used in a data-driven framework to build dynamic models of wind turbine gearboxes.An initial torsional model has been derived from first principles considering the stiffness of the gears, shafts, and structural components in the gearbox together with the mechanical components of the test bench. This model has been used to create simulated data of the experiments performed on gearboxes and to apply system identification techniques to the simulated signals, with a focus on predictor based subspace identification methods. System identification has been applied to torque and speed data measured on physical tests of two 3.4MW gearboxes. Gearbox excitation frequencies and their harmonics dominate the measured signals and disturb the system identification algorithms. Several techniques have been investigated to remove the shaft rotation and gear mesh frequency harmonics of the torque and rotational speed signals based on time synchronous averaging.

System identification of ship dynamic model based on Gaussian process regression with input noise

As a critical step designing the ship controller and the maritime traffic simulator, the system identification of a ship dynamic model from input-output data is a promising direction. However, the noise in the training data significantly reduces the accuracy in identification and prediction. Here, we present a robust nonparametric system identification technique for a ship maneuvering model based on Gaussian Process (GP) regression. To solve the problem caused by noise, the input noisy Gaussian Process (NIGP) model is employed, and it can automatically propagate the input uncertainty to the output in the learning model using the Taylor approximation. Polluted simulation datasets obtained from the numerical model of a parametric container ship are used for training. Zigzag and turning circle maneuvering motions are performed, and a comparison with GP and SVM is implemented to validate the proposed approach. The results indicate that the developed scheme for system identification of ships is accurate and robust with noisy input.

Applying polynomial decoupling methods to the polynomial NARX model

System identification uses measurements of a dynamic system’s input and output to reconstruct a mathematical model for that system. These can be mechanical, electrical, physiological, among others. Since most of the systems around us exhibit some form of nonlinear behavior, nonlinear system identification techniques are the tools that will help us gain a better understanding of our surroundings and potentially let us improve their performance. One model that is often used to represent nonlinear systems is the polynomial NARX model, an equation error model where the output is a polynomial function of the past inputs and outputs. That said, a major disadvantage with the polynomial NARX model is that the number of parameters increases rapidly with increasing polynomial order. Furthermore, the polynomial NARX model is a black-box model, and is therefore difficult to interpret. This paper discusses a decoupling algorithm for the polynomial NARX model that substitutes the multivariate polynomial with a transformation matrix followed by a bank of univariate polynomials. This decreases the number of model parameters significantly and also imposes structure on the black-box NARX model. Since a non-convex optimization is required for this identification technique, initialization is an important factor to consider. In this paper the decoupling algorithm is developed in conjunction with several different initialization techniques. The resulting algorithms are applied to two nonlinear benchmark problems: measurement data from the Silver-Box and simulation data from the Bouc-Wen friction model, and the performance is evaluated for different validation signals in both simulation and prediction.

Systematic reduced order model development of a pitching NACA0012 airfoil

Simple reduced order models (ROMs) for the aerodynamic coefficients - lift, drag, and pitch moment - of a pitching NACA0012 airfoil are presented. The ROMs are designed for quick computation of the transient aerodynamic characteristics of the airfoil and are developed utilizing computational fluid dynamics (CFD) simulation results. The entire aerodynamic system is modeled as a single input, multi output system yielding three independent systems to be characterized. A systematic, two step process is employed to develop the ROMs for each aerodynamic system. First, a CFD simulation is conducted to determine the linearity of each system, and any nonlinear system is restructured as a nonlinear operator followed by a linear system to allow for the use of linear system identification techniques. A second CFD simulation is conducted to determine the frequency response of each linear system, and the coefficients of each ROM are extracted by fitting a second order model to each frequency response function. The ROMs are validated against an independent CFD simulation of a pitching airfoil and are shown to accurately model each aerodynamic coefficient.

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

Reheat flames response to entropy waves

Sequential combustion architectures rely on auto-ignition flame stabilization in the second stage combustor. Due to the exponential temperature dependence of the ignition delay time the second stage flames can respond to hot spots generated by unsteady first stage combustion. In this paper, the auto-ignition flame response is investigated by extending an existing analytical model for acoustic waves so that it also accounts for entropy waves. Explicit expressions for the flame transfer functions and linear to non-linear transition thresholds are derived. Results for different operating conditions, forcing amplitudes and frequencies are discussed. For this, three computational domains of increasing complexity are considered for model validation: a 2D duct, a 3D backward facing step, and finally a full-scale Ansaldo Energia GT26 sequential combustor. Unsteady Reynold-averaged Navier–Stokes as well as large-eddy simulations are performed. Acoustic and entropic contributions of the flame response are separated by means of system identification techniques. Finally, non-linear flame transfer functions are identified. It is observed that the gains linearly increase with the excitation frequency and non-linear, frequency dependent effects are observed already for small amplitude levels of excitation.

Experiments and low-order modelling of intermittent transitions between clockwise and anticlockwise spinning thermoacoustic modes in annular combustors

Annular combustion chambers of gas turbines and aircraft engines are subject to unstable azimuthal thermoacoustic modes leading to high amplitude acoustic waves propagating in the azimuthal direction. For certain operating conditions, the propagating direction of the wave switches randomly. The strong turbulent noise prevailing in gas turbine combustors is a source of random excitation for the thermoacoustic modes and can be the cause of these switching events. A low-order model is proposed to describe qualitatively this property of the dynamics of thermoacoustic azimuthal modes. This model is based on the acoustic wave equation with a destabilizing thermoacoustic source term to account for the flame’s response and a stochastic term to account for the turbulent combustion noise. Slow-flow averaging is applied to describe the modal dynamics on times scales that are slower than the acoustic pulsation. Under certain conditions, the model reduces formally to a Fokker-Planck equation describing a stochastic diffusion process in a potential landscape with two symmetric wells: One well corresponds to a mode propagating in the clockwise direction, the other well corresponds to a mode propagating in the anticlockwise direction. When the level of turbulent noise is sufficient, the stochastic force makes the mode jump from one well to the other at random times, reproducing the phenomenon of direction switching. Experiments were conducted on a laboratory scale annular combustor featuring 12 hydrogen-methan flames. System identification techniques were used to fit the model on the experimental data, allowing to extract the potential shape and the intensity of the stochastic excitation. The statistical predictions obtained from the Fokker–Planck equation on the mode’s behaviour and the direction switching time are in good agreement with the experiments.

System Identification to Characterise Shoulder Joint Dynamics in Two Degrees of Freedom.

In this study, we present a new design of a shoulder perturbation robot that can characterise the dynamics of the shoulder in two degrees of freedom. It uses two linear electric motors to perturb the shoulder joint in internal/external rotation and abduction/adduction, and force and position sensors to measure the corresponding torque and angular displacement about the joint. System identification techniques are used to estimate the dynamics of the muscles around the joint. The advantage our apparatus offers over the existing ones is that it can efficiently transfer torque to the joint and measure its dynamics separately with minimal interference from soft tissues. We verified that the apparatus can accurately estimate joint dynamics by conducting tests on a phantom of known properties. In addition, experiments were conducted on a human participant. It has been demonstrated that the measured dynamics of participant's arm are repeatable. The potential impact of our apparatus is to be used in clinic as a diagnostic tool for rotator cuff injuries.

Identification and Control of a Nonlinear Soft Actuator and Sensor System

Soft robots are becoming increasingly prevalent, with unique applications to medical devices and wearable technology. Understanding the dynamics of nonlinear soft actuators is crucial to creating controllable soft robots. this letter presents a system identification process and closed-loop control of foldable HASEL (hydraulically amplified self-healing electrostatic) soft actuators. We characterized foldable HASELs with linear frequency response tests and modeled them using a linear superposition of static and dynamic terms. We also identified two responses of the system: an activation and relaxation response. Based on these two responses, we developed a dual-mode controller which was validated through closed-loop control using a capacitive elastomeric strain sensor wrapped around the actuator. Using this integrated sensor, we achieved step response rise times as fast as 0.025 s and settling times as fast as 0.17 s while under load. These system identification and control techniques can be applied to any HASEL-driven soft robot and could be applied to other soft actuators to enable controllable soft robots.

Therapeutic Effects of an Anti-Gravity Treadmill (AlterG) Training on Neuromuscular Abnormalities Associated with Spasticity in Children with Cerebral Palsy.

We aimed to characterize the therapeutic effects of Anti-Gravity Treadmill (AlterG) Training on neuromuscular abnormalities associated with spasticity in children with cerebral palsy (CP). Eighteen subjects were divided into two groups; AlterG and control. All subjects received up to 40 minutes of training 3 times a week for 8 weeks. The control group received conventional occupational therapy. The advanced parallel-cascade system identification technique was used to characterize the neuromuscular abnormalities associated with spasticity and separated its intrinsic and reflex components. Reflex stiffness gain (GR) and intrinsic stiffness gain (K) were used to track the therapeutic effects of training on neural and muscular abnormalities. Both K and GR were strongly positioned dependent; they varied linearly with the ankle angle at dorsiflexion. Their position dependence was quantified by fitting a linear model to K and GR over dorsiflexion positions. The evaluations were performed at four-time points; i.e. the baseline (before starting the training), 1 and 2 months after starting the training, and 1 month after the completion of the training to assess the persistent effects. We determined the changes in K and GR intercept and slope parameters over these 3 months to evaluate the therapeutic effects of training on neuromuscular abnormalities. The results revealed that all K and GR parameters decreased substantially following using AlterG training and these changes were greater than those observed in the control. The results also showed that these therapeutic effects were persistent to a high extent, particularly in the AlterG group. Our findings suggested that AlterG training could be considered as a robust therapeutic intervention to reduce neuromuscular abnormalities and manage spasticity.