[show abstract][hide abstract] ABSTRACT: Identifying multiple models from both static and dynamic data is an important problem in several engineering fields. Clustering based on Euclidean distance measure has been proposed to solve this problem. However, since Euclidean distance is not directly related to model fidelity, these approaches can lead to suboptimal results even when the number of models is known. In this work, through a three step algorithm that includes initialization, prediction error based fuzzy clustering and model rationalization, we evaluate the possibility of uncovering multiple model structures from data. The three step algorithm is also assessed for the identification of piecewise auto regressive exogenous systems with unknown number of models and their (unknown)orders. The basic approach can be extended for trend analysis and generalized principal component analysis.
International Journal of Advances in Engineering Sciences and Applied Mathematics. 08/2013; 4(1-2).
[show abstract][hide abstract] ABSTRACT: Faults lead to loss of productivity and in rare cases, loss of human lives. Therefore fault diagnosis is a critical task for increased reliability and safety. There are a variety of techniques that have been proposed in the literature for fault diagnosis. A comprehensive diagnostic problem is one which involves parametric changes in the presence of sensor failures and controller or actuator malfunction. In this paper we describe a diagnostic framework that integrates qualitative models with quantitative estimation to address the comprehensive diagnostic problem. The comprehensive diagnostic framework presented in this paper can be studied as four modules. The first module, also referred to as the diagnostic module, uses qualitative techniques to generate a hypotheses set of all possible root causes. In this paper we study a signed directed graph based qualitative model for the generation of hypotheses. The second module, called the hypothesis generator intelligently constructs and reorders the candidate hypothesis sets. The third module is the nonlinear estimation module, which estimates the relevant parameters in the candidate hypothesis. The fourth module performs statistical testing of the estimation results to either validate or invalidate the hypothesis. The efficacy of the proposed framework is demonstrated on simulation examples.
Chemical Engineering Research and Design 07/2013; 85(9):1281-1294. · 1.93 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this work, we present a generalized method for analysis of data series based on shape constraint spline fitting which constitutes the first step towards a statistically optimal method for qualitative analysis of trends. The presented method is based on a branch-and-bound (B&B) algorithm which is applied for globally optimal fitting of a spline function subject to shape constraints. More specifically, the B&B algorithm searches for optimal argument values in which the sign of the fitted function and/or one or more of its derivatives change. We derive upper and lower bounding procedures for the B&B algorithm to effi-ciently converge to the global optimum. These bounds are based on existing so-lutions for shape constraint spline estimation via Second Order Cone Programs (SOCPs). The presented method is demonstrated with three different examples which are indicative of both the strengths and weaknesses of this method.
Computers & Chemical Engineering 06/2013; · 2.09 Impact Factor
[show abstract][hide abstract] ABSTRACT: The ability to actively control the spatial and temporal dynamics of droplets in microfluidic networks can be harnessed for several applications. Achieving such control is nontrivial due to the nonlinear and interactive nature of such systems, where droplets in different branches of the network can affect each other through resistive signalling. In our previous work we have demonstrated the application of a Model Predictive Control (MPC) framework for sort-synchronization in a simple microfluidic loop device, assuming that the final control elements are elastomeric valves. In this paper, we explore the ability of the MPC framework for more intricate control, where the relative drop distances at the exit of a loop are required to conform to desired profiles. We demonstrate that through appropriate MPC objective function choices, a variety of digital signals based on the relative exit distance can be generated. The importance of such control is highlighted.
Journal of Process Control 02/2013; 23(2):132–139. · 1.81 Impact Factor
[show abstract][hide abstract] ABSTRACT: Droplets moving in a microfluidic loop device exhibit both periodic and
chaotic behaviors based on the inlet droplet spacing. We propose that the
periodic behavior is an outcome of a dispersed phase conservation principle.
This conservation principle translates into a droplet spacing conservation
equation. Additionally, we define a simple technique to identify periodicity in
experimental systems with input scatter. Aperiodic behavior is observed in the
transition regions between different periodic behaviors. We propose that the
cause for aperiodicity is the synchronization of timing between the droplets
entering and leaving the system. We derive an analytical expression to estimate
the occurrence of these transition regions as a function of system parameters.
We provide experimental, simulation and analytical results to validate the
[show abstract][hide abstract] ABSTRACT: It is well known that oscillations are a major cause for inferior product quality and productivity losses. Understanding the nature and the phenomena that underlie the oscillations is the first step in mitigating their effect on plant performance. Industrial reality is that multiple oscillations are generally present in the data due to several underlying sources. Detection of oscillations and identification of their time periods are difficult due to the presence of noise in data that might lead to spurious peaks in the power spectrum of the process output. This problem of oscillation detection has received much attention in the literature in recent years. In this paper, an oscillation detection approach that is based on processing of the intrinsic modes that are identified by the sieving process of Empirical Mode Decomposition (EMD) is proposed. The advantages of the proposed method are: (i) ability to detect the presence of single/multiple oscillations and identify their time periods, (ii) ability to provide the amplitude of oscillations, (iii) robustness to noise, (iv) capability to handle nonstationary trends and, (v) ability to provide information about dominant and weak oscillatory modes in the process data. Simulation studies demonstrate the robustness of the proposed approach to noise and its ability to characterize multiple oscillations in the process output. Results obtained from this approach on various industrial case studies are promising and seem to indicate that the proposed technique can be readily implemented in industrial environment.
Control Engineering Practice 08/2012; 20(8):733–746. · 1.67 Impact Factor
[show abstract][hide abstract] ABSTRACT: A new index is developed for assessing the performance of a single-input single-output (SISO) linear feedback control loop. The proposed metric is a specific scaling of the generalized Hurst exponent, computed through the method of detrended fluctuation analysis (DFA). We refer to this scaled exponent as the Hurst index. The new method compares favorably with the widely used minimum variance index (MVI), with both indices showing similar trends under changes in controller tunings during closed-loop simulations. The main advantage of the Hurst index over the MVI and other existing performance measures is that its determination does not require a priori knowledge of any loop parameters. Instead, computation of the index relies solely upon process output data collected during routine plant operation. Therefore, this new technique could potentially allow engineers to more efficiently identify problematic control loops.
[show abstract][hide abstract] ABSTRACT: Nonlinear constrained state estimation is an important task in performance monitoring, online optimization and control. There has been recent interest in developing estimators based on the idea of unscented transformation for constrained nonlinear systems. One of these approaches is the unscented recursive nonlinear dynamic data reconciliation (URNDDR) method. The URNDDR approach follows the traditional predictor-corrector framework. Constraints are handled in the prediction step through a projection algorithm and in the correction step through an optimization formulation. It has been shown that URNDDR produces very accurate estimates at the cost of computational expense. However, there are two issues that need to be addressed in the URNDDR framework: (i) URNDDR approach was primarily developed to handle bound constraints and needs to be enhanced to handle general nonlinear equality and inequality constraints, and (ii) computational concerns in the application of the URNDDR approach needs to be addressed. In this paper, a new estimation technique named constrained unscented recursive estimator (CURE) is proposed, which eliminates these disadvantages of URNDDR, while providing estimates with almost the same accuracy.
Journal of Process Control 04/2012; 22(4):718–728. · 1.81 Impact Factor
[show abstract][hide abstract] ABSTRACT: We investigate the dynamics of pairs of drops in microfluidic ladder networks
with slanted bypasses, which break the fore-aft structural symmetry. Our
analytical results indicate that unlike symmetric ladder networks, structural
asymmetry introduced by a single slanted bypass can be used to modulate the
relative drop spacing, enabling them to contract, synchronize, expand, or even
flip at the ladder exit. Our experiments confirm all these behaviors predicted
by theory. Numerical analysis further shows that while ladder networks
containing several identical bypasses are limited to nearly linear
transformation of input delay between drops, mixed combination of bypasses can
cause significant non-linear transformation enabling coding and decoding of
Microfluidics and Nanofluidics 11/2011; 14(1-2). · 3.22 Impact Factor
[show abstract][hide abstract] ABSTRACT: The primary objective in solving optimal input design problems is to obtain maximally informative inputs to be used as perturbation signals in system identification experiments. In plant-friendly identification, the designer has to respect constraints on experiment time, input and output amplitudes or input move sizes. This work focuses on plant friendly input design with constraints on input move size and output power. We present a convex relaxation to the problem of designing an informative input subject to input move size and output power constraints. The problem is finitely parametrized using ideas from Tchebycheff systems and reformulated as a SemiDefinite Programme.
[show abstract][hide abstract] ABSTRACT: Identification of stable parametric models from input-output data of a process (stable) is an essential task in system identification. For a stable process, the identified parametric model may be unstable due to one or more of the following reasons: 1) presence of noise in the measurements, 2) plant disturbances, 3) finite sample effects 4) over/under modeling of the process and 5) nonlinear distortions. Therefore, it is essential to impose stability conditions on the parameters during model estimation. In this technical note, we develop a computationally efficient approach for the identification of global ARX parameters with guaranteed stability. The computational advantage of the proposed approach is derived from the fact that a series of computationally tractable quadratic programming (QP) problems are solved to identify the globally optimal parameters. The importance of identifying globally optimal stable model parameters is high lighted through illustrative examples; this does not seem to have been discussed much in the literature.
IEEE Transactions on Automatic Control 07/2011; · 2.72 Impact Factor
[show abstract][hide abstract] ABSTRACT: A common practice in a system identification exercise is to perturb the system of interest and use the resulting data to build a model. The problem of interest in this contribution is to synthesize an input signal that is maximally informative for generating good quality models while being “plant friendly,” i.e., least hostile to plant operation. In this contribution, limits on input move sizes are the plant friendly specifications. The resulting optimization problem is nonlinear and nonconvex. Hence, the original plant friendly input design problem is relaxed which results in a convex optimization problem. We formulate a SemiDefinite Programme using the theory of generalized Tchebysheff inequalities to derive tight bounds on the quality of relaxation. Simulations show that the relaxation results in more plant friendly input signals.
IEEE Transactions on Automatic Control 07/2011; · 2.72 Impact Factor
[show abstract][hide abstract] ABSTRACT: The amount of current generated in a polymer electrolyte membrane fuel cell (PEMFC) depends strongly on the local conditions in a cathode such as available oxygen, surface area available for the reactions, amount of ionomer, and amount of electro-catalyst. In the present work, design parameters of a cathode catalyst layer are optimized to achieve the maximum current density at a given operating voltage. The decision variables are chosen such that they can be realized experimentally. To understand the effect of the model fidelity on the decision variables, optimization is performed with a single phase model and a two-phase model with and without membrane. Other objective functions such as maximization of current generation per catalyst loading, minimization of catalyst layer cost per power and minimization of cell cost per power are also considered to study the effects of the objective functions on the decision variables.
Chemical Engineering Research & Design - CHEM ENG RES DES. 01/2011; 89(1):10-22.
[show abstract][hide abstract] ABSTRACT: Proton exchange membrane fuel cells (PEMFCs) have strong potential as power conversion devices of the future, especially for man-portable and mobile applications. However, the manufacturing cost should be significantly reduced for making PEMFCs commercially attractive. An improvement of the power density with respect to the weight of the cell - termed as gravimetric power density in this study - can help in achieving lower manufacturing cost and reducing parasitic power losses, which is particularly important in man-portable applications. Furthermore, the power density of a PEMFC with respect to the overall volume of the cell - termed as volumetric power density in this study - must be improved for man-portable and automotive applications. The bipolar plates made out of graphite contribute significantly to the cost, weight, and volume of the cell. The state-of-the-art PEM fuel cells are of planar design. While several commercial planar prototypes have been demonstrated, cost and water management are still major issues. These problems arise partly as a result of the complicated bipolar plate design in planar PEMFC. Because the planar fuel cell concept has been so well-entrenched, alternate designs have not been seriously pursued. In this paper, we present some experimental studies on a novel cylindrical PEM fuel cell design that addresses the cost, gravimetric and volumetric power density issues. This study while highlighting the advantages of the tubular design also identifies areas of research that will have tremendous utility in further development of this technology.
International Journal of Hydrogen Energy 01/2011; 36(1):713-719. · 3.55 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper is concerned with the application of Kalman filter based methods for Fault Detection and Identification (FDI). The original Kalman based method, formulated for bias faults only, is extended for three more fault types, namely the actuator or sensor being stuck, sticky or drifting. To benchmark the proposed method, a nonlinear buffer tank system is simulated as well as its linearized version. This method based on the Kalman filter delivers good results for the linear version of the system and much worse for the nonlinear version, as expected. To alleviate this problem, the Extended Kalman Filter (EKF) is investigated as a better alternative to the Kalman filter. Next to the evaluation of detection and diagnosis performance for several faults, the effect of dynamics on fault identification and diagnosis as well as the effect of including the time of fault occurrence as a parameter in the diagnosis task are investigated.
Computers & Chemical Engineering. 01/2011; 35:806-816.
[show abstract][hide abstract] ABSTRACT: Engineered systems are increasingly equipped with sensing and actuating equipment making the operation and supervisory task increasingly difficult to handle by means of human interaction alone. In particular, the detection, identification and accommodation of abnormal, potentially harmful, events has been a long-standing challenge. Many scientists in different scientific areas have attacked this problem which has resulted in a plethora of techniques for both Fault Detection and Identification (FDI) and advanced control, each with their strengths and weaknesses. Because of the diverse nature of adopted theory and paradigms and because of a historical separation of FDI specialists and control theoreticians, it remains a challenge to establish automated systems able to handle exceptional events with minimal human intervention. As such, a project has been set up to enable full integration of diverse FDI methods as well as optimal coupling of FDI modules and control modules in the closed-loop supervisory control system. In this contribution, we introduce the basic paradigms of our approach, a strategic plan to achieve this goal as well as some preliminary results.
Resilient Control Systems (ISRCS), 2010 3rd International Symposium on; 09/2010
[show abstract][hide abstract] ABSTRACT: Solid oxide fuel cells (SOFCs) are high temperature fuel cells with a strong potential for stationary power house applications. However, considerable challenges need to be overcome to connect these cells to the power grid. The fluctuating grid demand has to be met without sacrificing the cell efficiency and causing structural/material damage to the system. This requirement coupled with fast and highly nonlinear transients of the transport variables results in a challenging control problem. This paper is on synthesizing a controller that can address some of these challenges. For using in the model predictive controller (MPC), input−output models are identified from the data generated by a detailed dynamic model. A traditional SISO control and a novel MIMO control are considered here. In the SISO control problem, power is the controlled variable (CV) and H2 flow is the manipulated variable (MV). In the MIMO control problem, power and the utilization factor (UF) of the fuel are the CVs while voltage and the flow of H2 are the MVs. The identification study shows that the nonlinear NAARX models with properly chosen cross terms can improve the model performance significantly in a MIMO problem. The results from the control study indicate that a well-tuned proportional−integral−derivative (PID) controller is sufficient for the single input single output (SISO) power control of a tubular SOFC. It also shows that the mutiple input multiple output (MIMO) control of power and the UF is highly interactive and necessitates a nonlinear model predictive controller (NMPC). Without using any additional hardware such as an ultracapacitor or battery pack, the designed NMPC could satisfy a step change in load with acceptable overshoot in power and the UF. A well-tuned PID controller is found to perform poorly for the MIMO problem. On the basis of these findings, future work will focus on the development of nonlinear predictive control approaches for stack-level control of tubular solid oxide fuel cells.
Industrial & Engineering Chemistry Research - IND ENG CHEM RES. 04/2010; 49(10).