Novel Substructural Health Monitoring Method without Interface Measurements using State Space Analysis and Extended Kalman Filtering

Abstract

Introduction Structural Health Monitoring (SHM) at its core is a process to identify damage, defined as either material or geometric change, of a system that negatively affects the systems performance[7]. SHM seeks to address four questions: (Level 1) Detection: Is damage present? (Level 2) Localization: What is the probable location of damage? (Level 3) Assessment: What is the severity of the damage? (Level 4) Prognosis: What is the remaining service life of the damaged system? SHM can be used to monitor structures affected by external stimuli, long-term movement, material degradation, or demolition. It can also assess integrity after natural disasters, lessen need for maintenance and repair construction, provide information to improve future designs, and encourage the move towards performance-based design philosophy instead of time-based design[3][7][9]. Multiple damage-detection methods are currently in practice including visual detection, acoustic emission, ultrasonic methods, magnet particle inspection, radiography, eddy-current methods and thermal field methods[7][18]. These methods, though, require prior knowledge of the damage location and that the inspected area is readily accessible. Therefore, research in the field of vibration-based inspection has significantly increased in the past two decades as a way to detect damage. Damage can alter stiffness, mass, and energy dissipation, which result in a change in the dynamic characteristics of a system including natural frequencies, mode shapes, modal curvatures, and flexibility[7][20]. SHM methods identify one or more of these features using data obtained from sensors attached to the system being investigated. Then using prior data from the undamaged state or from computer modeling, irregular behavior is identified and statistical approaches are used to detect damage. Each identified feature has its own advantages and disadvantages such as sensitivity to noise and environmental effects, ease of calculation, and ability to answer the higher leveled questions mentioned above. The majority of methods for structural health monitoring have observed the whole or global system. Recently there has been a shift to methods focusing on smaller substructures of the system due to several reasons. Classical global methods have been tested on small structural systems comprised of only a few unknowns and degrees-of-freedom. In contrast, real-world structures are becoming larger and more advanced. Due to ill-conditioned behavior in many global monitoring approaches, the large increase in DOFs can create problems in convergence[12][15][25]. In addition, local damage can introduce insignificant changes in global modal data[25]. A division of the whole structural system into smaller substructures lessens the amount of unknowns and can treat local areas as isolated structures, therefore allowing for more accurate results, improved identification of local damage, and easier convergence to a solution[12][15]. Other advantages include the need for smaller amounts of sensors, reductions in modeling errors due to the smaller size and reduced complexity, and minimizing the need for expensive computation otherwise needed for processing large amounts of data[15]. Lastly, the use of substructures can allow for output-only identification if the excitation is random or located only outside the substructure; this is advantageous in the common case where ambient excitation is used for identification[15]. With both global and substructural methods, many problems arise due to variability in the field, test procedures, data manipulation, as well as assumptions made and uncertainties of many aspects involved. As opposed to computer models and laboratory testing, field systems are affected by environmental and operational factors that impact SHM results such as temperature, humidity, changing boundary conditions, ambient loading conditions, and mass loading effects[20]. Each of these factors can significantly change the response of a system potentially leading to a false-positive identification of damage. Methods should seek to understand these factors so to avoid this problem. Generating input forces can also produce challenges for monitoring. Ideally, testing of a system should not take a bridge out of service. To achieve this, ambient excitation, though impossible to measure, should be used and the chosen SHM methods should be able to use output-only data[7][9]. Ambient excitation cannot be dictated, so certain vibration modes may not be excited and as a result not identifiable from the data[7]. Furthermore, almost all SHM methods developed assumed linear behavior, which is not always the case[7]. In nonlinear behavior, modal parameters are sensitive to excitation amplitude, which may again falsely detect damage[9]. Related to linearity of a system, numerical models represent linear behavior that may not be accurate. Additional challenges with models arise with material properties and connections that are hard to verify and heterogeneity of systems which are hard to replicate[10]. This paper proposes a substructural monitoring method able to detect the presence of damage using the Extended Kalman Filter and State-Space analysis. The novelty of this method is in the simulation of the interface degrees of freedom with modal coordinates for the global structure, such that internal forces at the interface need not be calculated and measurements of the motion at the interfaces need not be collected. This method’s objectives are to 1. Minimize the need for interface measurements, but also leverage interface measurements if available by modeling internal forces as ”ground motions” at the interfaces 2. Avoid the requirement of measuring external forcing by regarding unknown forcing as noise and estimating its value for each time step 3. Identify directly the stiffness of the structure as the damage-relevant feature for SHM 4. Operate efficiently and in near real-time This thesis will discuss other methods and research in the field of substructural health monitoring, provide a walk-through of the theory behind the proposed substructural monitoring method, discuss application of the method on numerical models and its results, and then conclude with a summary, setbacks, and potentials of this method.