Identification of Transverse Crack in a Cracked Cantilever Beam Using Fuzzy Logic and Kohonen Network

The issue of crack detection and diagnosis has gained wide spread industrial interest. Crack/damage affects the industrial economic growth. Generally damage in a structural element may occur due to normal operations, accidents, deterioration or severe natural events such as earth quake or storms. Damage can be analyzed through visual inspection or by the method of measuring frequency, mode shape and structural damping. Damage detection by visual inspection is a time consuming method and measuring of mode shape as well as structural deflection is difficult rather than measuring frequency. As Non- destructive method for the detection of crack is favorable as compared to destructive methods. So, our analysis has been made on the basis of non-destructive methods with the consideration of natural frequency. Here the crack is transverse surface crack. In the current analysis, methodologies have been developed for damage detection of a cracked cantilever beam using analytical, fuzzy logic, kohonen network as well as experimental. Theoretical analysis has been carried out to calculate the natural frequency with the consideration of mass and stiffness matrices. The data obtained from theoretical analysis has been fed to fuzzy controller as well as the kohonen competitive learning network. The Fuzzy Controller uses the different membership functions as input as well as output. The input parameters to the Fuzzy Controller are the first three natural frequencies. The output parameters of the fuzzy controller are the relative crack depth and relative crack location. Several Fuzzy rules have been trained to obtain the results for relative crack depth and relative crack location. Kohonen network is nothing but a competitive learning network is used here for the detection of crack depth and location. It is processed through a vector quantization algorithm. A comparative study has been made between fuzzy logic technique and Kohonen network technique after experimental verification. It has been observed that the process of kohonen network can predict the depth and location accurately as close to fuzzy logic technique

Comparison of CFBP, FFBP, and RBF Networks in the Field of Crack Detection

The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks used are cascade forward back propagation (CFBP) network, feed forward back propagation (FFBP) network, and radial basis function (RBF) network. In the first phase of this paper theoretical analysis has been made and then the finite element analysis has been carried out using commercial software, ANSYS. In the second phase of this paper the neural networks are trained using the values obtained from a simulated model of the actual cantilever beam using ANSYS. At the last phase a comparative study has been made between the data obtained from neural network technique and finite element analysis