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In this paper, an analytical model is proposed to predict magnetic flux leakage (MFL) signals from the surface defects in ferromagnetic tubes. The analytical expression consists of elliptic integrals of first kind based on the magnetic dipole model. The radial (B z ) component of leakage fields is computed from the cylindrical holes in ferromagnetic tubes. The effectiveness of the model has been studied by analyzing MFL signals as a function of the defect parameters and lift-off. The model predicted results are verified with experimental results and a good agreement is observed between the analytical and the experimental results. This analytical expression could be used for quick prediction of MFL signals and also input data for defect reconstructions in inverse MFL problem.

This paper proposes a nonlinear optimal-function-based algorithm which can be utilized to replace electronic circuitry traditionally employed to linearize the characteristics of commonly used temperature transducers such as resistance temperature detectors, thermistors and thermocouples. The function exploits ratiometric-logarithmic operation for linearization. The optimal parameters of the function are determined using a covariance matrix adopted evolutionary strategy (CMAES) algorithm. Transducers' input–output data are derived from the Yokogawa handy calibrator model CA 150 and subjected to the proposed algorithm to evaluate the performance of the method. The performance measures such as full-scale error and mean square error are considered to compare the performance of the proposed technique with other methods reported for transducers. The present linearization algorithm was implemented using LabVIEW 7.1 Professional Development System in a personal computer that provides the facility to interface with the National Instruments data acquisition module NI DAQCard PCI-6221. Experimental results reveal that the proposed evolutionary optimized nonlinear function based software linearizer does its job efficiently in a better way than that of the conventional hardware and software methods. Also, the results obtained using the CMAES algorithm are compared with the results of a real-coded genetic algorithm. The comparison shows that the CMAES algorithm is more consistent in determining the best solution for the proposed ratiometric-logarithmic function with reasonable computation time.

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Steam generator tubes and Obscured pipe lines like sewers, water mains have to be checked for their current condition. Cracks and defects are a strong indicator for the condition of a pipe. Electromagnetic nondestructive tests are important and widely used within the field of nondestructive evaluation (NDE). Magnetic Flux Leakage (MFL) has grown into a crucial method for inspection of pipelines and tubing in order to prevent long-term failures. Digital image processing techniques open the opportunity to accelerate the image analysis process, which may ease the operator from a lot of tedious task. An affordable way to detect those cracks is to take images of the pipeline and use image processing techniques to detect defects in these images. The Magnetic flux leakage images obtained from simulation software COM SOL multi physics 4.3a are used for this work. Automatic segmentation is an important technique in the image processing. The basic idea of segmentation is to automatically select on gray-level values for separating object from the background. In this work, median filter is used to pre process the raw NDT image and three segmentation techniques are performed to segment the defect from the defective steam generator tube images. The performance evaluation of three segmentation algorithms namely region growing, minimum error thresholding and Morphological segmentation method for Non Destructive testing (NDT) are performed and compared. Region growing technique is performed well for almost all MFL images.