Genetic Algorithms-Based Parameter Optimization of a Non-Destructive Damage Detection Method

Non-destructive testing (NDT) is an important area of research, dealing with diagnostic and monitoring the health of structures and structural components and preventing catastrophic failures. One of the recently developed NDT techniques is the method of strain energy mode shapes that allows the determination of changes in structural integrity from changes in the vibrational response of a structure. Normally, application of the strain energy mode shape method requires knowing the state of a structure before it was damaged, which limits the range of the method’s applicability. A modification of the strain energy method has been developed at West Virginia University. The modified (non-baseline) method does not require knowledge of the undamaged state of a structure (baseline). It has been theoretically proven that for simply supported and free-free boundary conditions the damage response can be extracted in the frequency domain by high-pass filtering. However, the filtering operation creates some distortion in the damage indicators. Some low-level damages may be “masked” by this distortion. Additionally, the theory can only predict the cut-off frequency of the filter, but cannot estimate its optimal amplitude characteristic. In this study, genetic algorithms (GA) have been applied to produce a sufficiently optimized amplitude characteristic of a filter used to extract damage information from strain energy mode shapes. Finite element modeling has been used to produce a training data set with the known location of damages. The amplitude characteristic of the filter has been encoded as a genetic string where the pass coefficient for each harmonic of its Discrete Fourier Transform representation is a number between zero and one in 8-bit Gray code. The genetic optimization has been performed based on the minimization of the signal-to-distortion ratio. The amplitude characteristic of the filter was not limited to any specific configuration, i.e. either lowor highpass or specific cut-off frequencies. The results obtained from the GA confirmed the theoretical predictions and allowed improvement in the method’s sensitivity to damages of lower magnitude. I. INRODUCTION This paper presents an interdisciplinary research in which genetic algorithms (GA) have been used both to confirm theoretical findings and to find a near-optimal solution for a real-world problem within the framework of designing an automated damage detection system. The original problem comes from the area of NonDestructive Testing and Evaluation (NDTE). Methods and techniques of NDTE generally deal with detection and location in structures, machinery, etc. Early detection allows prevention of catastrophic failures that lead to economic loss and possible loss of human life. A recent project at West Virginia University had the goal of developing an automated damage detection system for Armored Vehicle Launched Bridge [1, 2]. A modal (vibration-based) method of nondestructive testing known as the Strain Energy Mode Shapes (SEMS) method was used to detect and locate damages in AVLB. Despite being a relatively new method, the strain energy method has good coverage in the research literature [3-9]. Strong features of this method include its ability to both detect and locate damage with high precision; its high sensitivity to low-level damages and relatively simple acquisition if the initial data [6]. At the same time this method suffers from its high sensitivity to measurement noise and requires knowing the original (undamaged) state of the structure before performing any tests. The latter significantly limits applicability of the method. Consider a generic procedure involving the SEMS method (Fig. 1): 1. Vibration data are acquired on the undamaged (baseline) structure. The strain energy mode shapes are computed using the following formula: