SYSTEM IDENTIFICATION TOOLBOX FOR MATLAB: DEVELOPING A GRAPHICAL USER INTERFACE. Rebecca L. Leonard, University of Arkansas

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1 SYSTEM IDENTIFICATION TOOLBOX FOR MATLAB: DEVELOPING A GRAPHICAL USER INTERFACE Rebecca L. Leonard, University of Arkansas REU Institution: Lehigh University Principal Investigator: Dr. Shamim N. Pakzad Graduate Mentor: Minwoo Chang

2 Abstract System identification algorithms, such as the Eigensystem Realization Algorithm (ERA) and the Auto-Regressive Moving-Average method with exogenous terms (ARMAX), make it possible to identify a system s modal parameters based on its vibrational response to a disturbance. Because these methods can be tedious and complex to implement, there is a need for a comprehensive software package that simplifies the computational process. A System Identification Toolbox for MATLAB (SITM) was created to fill this void and allow for the comparison of seven different system identification methods. A graphical user interface (GUI) was developed to enhance the usability, speed, reliance, and simplicity of SITM. This paper will focus on the process of developing a GUI for SITM and the optimization of the code to work with the GUI as quickly and efficiently as possible. 2

3 Table of Contents 1. Introduction Research Background and Literature Review Project Objectives Methods Content Layout Coding Testing SITM GUI Performance User Polling Documentation SITM User Process Results Functionality Confirmation User Feedback Conclusion References Acknowledgements

4 1 Introduction 1.1 Research Background and Literature Review In the past decade, advances in micro-electro-mechanical systems (MEMS) technology have paved the way for the development of multifunctional sensor nodes that make up a wireless sensor network (WSN) (Pakzad et al. 2008). These nodes are less expensive to install and maintain than wired sensor networks, and can also collect data at a higher spatial and temporal resolution (Pakzad et al. 2010). Wireless sensor networks can be used to monitor things like humidity, temperature, lighting, pressure, vehicular movement, soil composition, noise, and mechanical stress, among other things (Akyildiz et al. 2002). A significant application in the civil engineering industry is the use of WSNs for structural health monitoring. Researchers can use data collected by strategically-placed sensors to identify a structure s modal parameters, i.e. natural frequencies, damping ratios, and mode shapes (Pakzad et al. 2010). Structural damage affects a system s modal parameters; therefore, these parameters may be traced over time to monitor the health of the system (Chang and Pakzad 2011). The center for Advanced Technology for Large Structural Systems (ATLSS) at Lehigh University housed an experiment to evaluate the performance of a WSN by comparing the quality of influence coefficients and the rate of convergence of estimated parameters of a scaled specimen of a steel beam-column connection subjected to harmonic excitations. The performance of the WSN was deemed acceptable and fairly capable of detecting structural damage. The WSN was tested against a wired network and detected damage just as well as the wired sensor network (Dorvash et al., 2010). Over the past three decades, several system identification algorithms have been published by various authors. System identification is the process of constructing mathematical models of dynamic systems based on analysis of the system s input and output signals (MathWorks 2009). The Eigensystem Realization Algorithm (ERA), proposed by Juang and Pappa (1985) is one of the most widely used system identification methods to date, and has served as a basis for the formation of other algorithms (Chang and Pakzad 2011). Based on these system identification algorithms, a software toolkit, called System Identification Toolbox for MATLAB (SITM), was developed to identify modal parameters of a structure by processing the input/output or output only of a system (Chang 2011). Users of SITM are able to investigate true structural modes, physical mode shapes, and modal parameters based on data gathered by wireless sensor networks (WSNs). MATLAB was chosen for the development of SITM because it meets the following criteria: (1) ability to process eigenvalue/eigenvector and singular value 4

5 decomposition (SVD) functions, (2) ability to formulate large matrices and create variables without pre-allocation of memory, (3) possession of built-in functions for creating graphical results, and (4) compatibility with all major operating systems, including Microsoft Windows, Apple Mac OS, and other UNIX systems (MathWorks 2009). SITM allows users to choose from the seven different system identification methods shown in Table 1, depending on the data type of their system (Input/Output vs. Output Only). Results are displayed in a stabilization diagram, which allows users to graphically extrapolate true structural modes of a system (Chang 2011). Table 1: SITM s system identification methods by data type applicability. Input/Output systems are denoted I/O, and output only systems are denoted OO. Note that although there is an output only application of SRIM, that method is not presently an option in SITM (Chang 2011). ERA ARMAX SRIM N4SID I/O OO I/O OO I/O I/O OO ERA (impulse response only) ERA-OKID ERA-OKID ERA-NExT ARX AR SRIM N4SID N4SID ERA-NExT-AVG 1.2 Project Objectives Although SITM is already in a working state, the current challenge is the optimization of the software and the development of SITM GUI, a graphical user interface (GUI) that will enhance the usability, speed, reliance, and simplicity of the toolbox. Equipping a program with an effective GUI will save the user a considerable amount of time and money (Galitz 2002). MathWorks has published an online manual to help programmers develop GUIs for MATLAB (MathWorks 2009). The System Identification Toolbox for MATLAB will first be tested within the civil engineering department at Lehigh University. Any criticisms and/or suggestions will be considered for SITM 2.0, the second version of the toolbox. At that point, SITM may be released to a larger user pool. Ideally, SITM will serve as a tool for both novice and experienced engineers, alike, to explore the topics of structural health monitoring and system identification. Ultimately, the goal for SITM to become a leader in structural health monitoring. 5

6 2 Methods 2.1 Content The content required for inclusion in the GUI was clearly defined from the beginning of the project. In fact, SITM already technically had a GUI (Figure 1) before the official GUI project began in June Figure 1: Original SITM GUI with a question/answer format displayed directly in the MATLAB command window. Before work began on SITM GUI, it was known that the program would have to prompt the user for the following information: Data type Data files Geometric information, response direction System identification method Sampling frequency Filtering method, parameters Model order information Stabilization parameters Mode shapes to display It was also known that the program would display (a): Stabilization diagram Power spectral density (PSD) diagram Mode shape plots 6

7 Given this information, the challenge became designing a user-friendly layout that met all requirements in the most simplistic and efficient way possible. 2.2 Layout When SITM is launched, the user must choose either to load a previous system identification result or to begin a new session. This option is presented in the welcome window (Figure 2), which also displays the program title, developer information, and sponsorship acknowledgements. Figure 2: Welcome window that displays when SITM is launched from the MATLAB command window. The entire input (pre-processing) procedure is displayed in one window (Figure 3) to allow users to better anticipate and review required information and responses. Panels (displayed as cells with borders) organize the pre-processing window. The input information appears in the same order that SITM stores and calls the data, which reflects the most logical flow of thought. Although it is not desirable to have black space in a GUI, this layout still embodies the most simplistic input procedure. Additionally, 7

8 Figure 3: SITM GUI pre-processing window that is displayed when New Session is selected from the welcome screen. SITM GUI 2.0 is expected to possess new capabilities, i.e. an image-based method of defining user geometries; therefore, this blank space may be filled at a later time without having to change the entire layout of the GUI. The post-processing procedure is also entirely housed within its own window (Figure 4). This way, the user can define stabilization parameters, view the stabilization diagram, select (frequency, model order) pairs from the stabilization diagram, and view the respective mode shape plots in the same window. Pushbuttons, labeled Open in new window, appear in the upper, left-hand corner of the stabilization diagram and Mode Shapes panel once plotting is complete. These buttons open the respective object in a new window the same size as the post-processing window. This makes the object easier to read, and also allows the user to save only the desired object as a.fig file. 8

9 Figure 4: Post-processing window that displays either (1) after loading previous system identification results, or (2) after the system identification process is completed during a new session. 2.3 Coding In order to simplify interaction between the core code and the graphical user interface, SITM GUI was also developed within MATLAB. All SITM GUI code was prepared from scratch, without using MATLAB s open layout GUI editor, GUIDE. It took five weeks to finish the SITM GUI first draft. SITM GUI is imbedded within the 19 SITM (.m) files and communicates with the original code by passing variables between functions. The SITM GUI window size is set to 900 x 600 pixels, which should accommodate all desktop and laptop screen sizes. All objects are placed and sized in the window (using the lower, left corner of the object as the origin) by assigning the [x-coordinate, y- coordinate, height, width] in pixels. Table 2 describes user interface objects used in SITM GUI. Objects have customizable properties, i.e. font size, font name, horizontal alignment, background color, position, etc. that may vary depending on object type. 9

10 Table 2: Describes MATLAB user interface objects used in SITM GUI. Note that MATLAB offers more user interface objects than are shown in this table. (MathWorks 2009) Object uipanel uibuttongroup uicontrol Function groups objects, user can interact directly with interface controls groups buttons, exclusive selection for radio buttons and toggle buttons that it contains creates user interface control object, i.e. pushbutton, radio button, slider, editable text, static text Some sample lines of code are shown below to demonstrate how objects are created and properties assigned. # Creates Filtering Options button group that is sized and positioned # in terms of pixels. The title is displayed in Helvetica font, and # the border is etched out, 1 pixel wide, and black in color. The # visibility is set to off in order to force all objects to appear # at once at the end of the script. The SelectionChangeFcn property # commands the interface to perform certain actions, depending on # which radio button is selected. filtering_bg = uibuttongroup('units', 'Pixels',... 'Position',[ ],'Title','Filtering Options',... 'FontName','Helvetica',... 'BorderType','etchedout','ForegroundColor','Black',... 'ShadowColor','Black',... 'BackgroundColor',[1 1 1],'BorderType','etchedout',... 'BorderWidth',1,'Visible','off',... 'SelectionChangeFcn',@highlight_filtering_fn); # Creates a radio button that is sized and positioned in terms of # pixels. The button string, Filter Off, appears in 10 point, # Helvetica font, and is left-aligned in front of a white background. # This radio button is assigned to filtering_bg, and is tagged off # for future reference in input_info.filtering_off_rb = uicontrol('style','radio',... 'Position',[ ],... 'FontName','Helvetica','FontSize'10,... 'HorizontalAlignment','Left',... 'BackgroundColor',[1 1 1],... 'String','Filter Off','Parent',filtering_BG,... 'Tag','off'; 10

11 Each system identification method also had to be individually programmed into SITM. Figure 5 illustrates the computational process behind the Eigensystem Realization Algorithm (ERA) method. The other system identification methods follow the same basic idea, but include their own customizations. Figure 5: Flowchart illustrating the computational process behind the Eigensystem Realization Algorithm (ERA) method, which is one of the system identification methods available in the System Identification Toolbox for MATLAB. 11

12 2.4 Testing SITM GUI Performance In order to ensure that all functions were properly adapted, SITM GUI was tested against the original SITM code (which has already been extensively tested) any time an major update was applied. These tests consisted of examination and comparison of result files to make sure the files were equal. Development resumed only after it was confirmed that the program was working properly. 2.5 Usability Testing A meeting of 15 civil engineering graduate students at Lehigh University was called to examine the usability of SITM GUI. These potential users were given a demonstration of SITM GUI and each student was then verbally presented with the following questions: Do you think SITM GUI allows you to perform necessary functions? Are you more likely to use the new version of SITM than the older version of SITM? Do you believe that the features of the new version of SITM are relevant to the program s purpose? Would you say that SITM GUI has made SITM more user-friendly? 2.6 Documentation The 30-page user guide, instructing users on how to use SITM, was prepared in Apple Pages. The table of contents is shown in Table 3. Table 3: System Identification Toolbox for MATLAB User Guide table of contents. Topic Page Introduction 4 Getting Started 6 Using SITM 14 Interpreting Results 21 Troubleshooting 25 Appendix 28 References 29 The content and explanation techniques used in the SITM User Guide are loosely based off of the MathWorks (2009) online help documentation. 12

13 2.7 SITM User Process The program is launched by typing SITM in the MATLAB command window. Upon launch, the SITM welcome window will appear and prompt the user to choose either to load a previous system identification result (.mat file) or to begin a new session. The pre-processing window, as shown in Figure 3, contains seven panels: (1) Data Type, (2) Geometric Information, (3) Response Direction, (4) SI Method, (5) Sampling Frequency, (6) Filtering Options, and (7) Model Order Options. The Data Type panel is where users can specify whether they would like to process input and output data files or an output data file only. If the Output Only System radio button is selected, the Get Input Data File pushbutton and its editable text box will disappear, since no input data file is required in this case. When either the Get Output Data File or Get Input Data File pushbutton is selected, a browser window will appear where the user can select a data file containing input/output information that should be stored in a.dat file. The file name will automatically appear in the editable text boxes upon selection to verify that the correct file was selected. Since the usable methods depend on whether the system is input/output or output only, the system identification method options in the SI Method panel automatically change to reflect the current selection in the Data Type panel. If the user selects Process without having selected an input and/or output file, an error box will appear and SITM will not proceed until the required files have been selected. Within the Geometric Information panel, the user can select the structural geometry that best describes their system. The user-defined geometry information is stored within three editable text files that define connectivity, node information, and sensor location information. The Response Direction panel is only visible when User-Defined is selected, because the response directions are already set for simple shear structures and simple bridge systems. Future plans include the creation of an image-based method of defining new geometries. The SI Method panel houses the system identification methods that reflect the current data type selection. When Input/Output System is selected the SI Method panel will display the following system identification method options: ERA-OKID, ERA, ARX, SRIM, and N4SID. When Output Only System is selected, the SI Method panel will display the following system identification method options: ERA-OKID, ERA-NExT, ERA-NExT-AVG, AR, and N4SID. The Sampling Frequency panel contains an editable text box, where the user can input the sampling frequency at which the sensors collected the selected data. If Input PSD is selected, a power spectral density diagram will appear within the pre-processing window. If the user selects Process without having entered a sampling frequency, an error box will appear and SITM will not proceed until a sampling frequency is entered. Note that an output file must have already been selected in order for a PSD diagram to appear. 13

14 SITM s Filtering Options panel allows the user to choose whether or not they would like to filter their data and which method they would like to use to do so. If Filter Off is selected, the radio buttons in the second and third columns will be invisible. If Filter On is selected, SITM GUI displays the second and third columns that contain the filtering options and filtering parameters, respectively. Alternating between filtering methods in the second column will cause objects in the third column to appear and disappear, depending on what information is required for that filtering method. If the user selects Process without having entered a cutoff frequency, filtering order, or amplitude loss value, an error message will appear. SITM will not proceed until a valid value has been entered for each filtering parameter. The cutoff frequency must not exceed half of the current sampling frequency value; filtering order and amplitude loss must be a positive integers. The default cutoff frequency, filtering order, and amplitude loss values, specific to the selected filtering method, will automatically appear in the editable text boxes. The Model Order Options panel allows users to define the y-axis of the stabilization diagram that will appear in the post-processing window. The user should keep in mind that increasing the number of model order values to be processed will increase the time required to complete the computation. Additionally, higher model orders take longer to process than do lower model orders. The minimum model order may not exceed the maximum model order, and the increment must divide evenly into the difference between the maximum and minimum model orders. If the user selects Process without having entered valid maximum and minimum model order and increment values, an error message will appear, and SITM will not proceed until the user fixes the error. Selecting Process will begin the system identification process. Once the process is complete, a browser window will appear, prompting the user to name the.mat file that will store the workspace variables. The user should choose a descriptive name in order to easily recall project details. The user can load these results at a later time by selecting Load Previous in the welcome window. After the user saves the workspace variables, a window will appear where the user may choose either to continue to the post-processing procedure or to quit SITM. The post-processing window contains 3 panels: (1) Stabilization Parameters, (2) Mode Shape Options, and (3) Mode Shapes. The first panel houses editable text boxes, where users can enter stabilization parameters, including convergence thresholds for Frequency, Modal Assurance Criteria (MAC), and Damping Ratio. The user may also enter the minimum and maximum values for the x-axis of the stabilization diagram in the editable text boxes Min. Frequency and Max Frequency, respectively. When See Plot is selected, a stabilization diagram reflecting the values inputted by the user will appear in the black space to the right of the Stabilization Parameters panel. The Frequency, MAC, and Damping Ratio values must all be between 0 and 1. The Max Frequency must be greater than the Min. Frequency, and the Max Frequency must not exceed the Filtering Frequency defined in the pre-processing window. If the user selects See Plot 14

15 without having entered valid stabilization parameters, an error box will appear and SITM will not proceed until the user has fixed the error. As mentioned before, a pushbutton will appear in the upper, left-hand corner of the stabilization diagram, labeled Open in new window. This button will immediately open the stabilization diagram in a new window the same size as the post-processing window. This makes the diagram easier to read, and also allows the user to save only the stabilization diagram as a.fig file. The Mode Shape Options panel is where users can select which mode shape plots they wish to view. The user may choose between three options: (1) view all mode shapes at a specific frequency, (2) view all mode shapes at a specific model order, and (3) select points from the stabilization diagram and view their mode shape plots. If a pushbutton is selected before a valid value is entered into either of the editable text boxes, an error box will appear and SITM will not proceed until the user has corrected the error. The final panel, Mode Shapes, is where mode shape plots will appear after the user has selected a mode shape option. As previously mentioned, a pushbutton will appear in the upper, left-hand corner of the Mode Shapes panel, labeled Open in new window. This button will immediately open the mode shape plots in a new window the same size as the post-processing window, just as is possible with the stabilization diagram. Again, this makes the plots easier to read, and also allows the user to save only the mode shape plots as a.fig file. If more mode shape plots appear than can fit in the window, a scroll bar will automatically appear to simplify browsing. 15

16 3 Results 3.1 Functionality Confirmation This project successfully produced a working system identification program (SITM), complete with a functional GUI. Please see Figures 1-3 for screenshots of the final SITM product. All functionality testing confirmed that the new version of SITM is 100% consistent with the original version in producing results. 3.2 User Feedback Of the 15 potential SITM users polled, 100% answered yes to all of the questions mentioned in the Methods section. The students also verbally asserted that the current version of SITM (with SITM GUI) is much more versatile, functional, and reliable than the original version. Suggestions were made to add an image-based method of defining user geometries, which is planned for inclusion in SITM GUI

17 4 Conclusion Computer programs are most effective when they possess a user-friendly GUI. MATLAB users can create GUIs for programs that they write, by either using MATLAB s GUI Layout Editor (GUIDE) or writing code from scratch. The System Identification Toolbox for MATLAB, accompanied with SITM GUI, is an efficient MATLAB program that simplifies the system identification process and allows for quick comparison of methodology. If used to track changes in modal parameters, SITM can be used as a tool to monitor structural health. The System Identification Toolbox for MATLAB should be available to a wider user pool by

18 5 References 1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless Sensor Networks: A Survey. Computer Networks Elsevier Journal, Vol. 38, No. 4, Chang, M. (2011) Modal Identification Using SITM, Ph.D. thesis, Civil Engineering Dept., Lehigh University, Bethlehem, Pennsylvania. 3.Chang, M., and Pakzad, S. N. (2011). Modified Natural Excitation Technique for Stochastic Modal Identification. Journal of Structural Engineering, In Review. 4. Dorvash, S., Pakzad, S. N., Labuz, E. L., Chang, M., Li, X., and Cheng, L. (2010) Validation of a wireless sensor network using local damage detection algorithm for beam-column connections. Proceedings of the SPIE Sensors and Smart Structures Technologies, San Diego, California. 5. Galitz, W. O. (2002). The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques, Wiley, New York. 6. Juang, J. N., and Pappa, R. S. (1985). An eigensystem realization algorithm for modal parameter identification and model reduction. Journal of Guidance, Control, and Dynamics, 8: MathWorks, Inc. (2009). MATLAB 7 Getting Started Guide. < 8. Pakzad, S. N., Fenves, G. L., Kim, S., Culler, D. E. (2008). Design and Implementation of Scalable Wireless Sensor Network for Structural Monitoring. Journal of Infrastructure Systems, March Pakzad, S. N., Rocha, G. V., and Yu, B. (2010). Distributed modal identification using restricted auto regressive models. International Journal of Systems Science, In Press. 18

19 6 Acknowledgements Thanks to the center for Advanced Technology for Large Structural Systems (ATLSS) at Lehigh University for housing this research project during the summer of Thank you to the National Science Foundation and the George E. Brown Jr. Network for Earthquake Engineering Simulation for funding this project through grant EEC and creating a meaningful Research Experience for Undergraduates program. I would like to offer my sincere thanks to Dr. Shamim N. Pakzad and Minwoo Chang for their mentorship and also for allowing me to be creative with this project. Any opinions, conclusions, or recommendations expressed here are my own and do not necessarily reflect those of the sponsors. 19