Magnetic Circuit Design Based on Circumferential Excitation in Oil-Gas Pipeline Magnetic Flux Leakage Detection
In the oil-gas pipeline magnetic flux leakage (MFL) detection, the qualitative and quantitative analysis can be achieved. The circumferential excitation technology is an effective way to detect or size axial direction of defects reliably; Magnetic circuit design has become the key part of this technology to meet the magnetized requirements of circumferential excitation. This paper studies the magnetized section of MFL detector of the oil pipeline. With combination of analytical method and magnetic segmentation method, the Kirchhoff magnetic equation was established with MATLAB to design a magnetic circuit calculation model. Take f325´8 pipeline as an example, calculations are carried out to choose suitable geometry parameters and material properties for magnetic circuit design. And simulation by large-scale finite element software ANSYS is carried out to verify the correctness of magnetic circuit design. It has been testified in the results that the design of magnetic circuit has completely met the requirements of circumferential magnetization.
Imitating Research on Magnetic Flux Leakage testing of Pipeline Using ANSYS
Magnetic leakage field theory in magnetic flux leakage(MFL) testing of pipeline using finite element method was researched, by which MFL conditions of pipeline defect could be imitated through fair and foul, and compensate for the limitation of magnetic dipole method. The analyzing process of magnetic leakage field using finite element analyzing software ANSYS was introduced. The effect of lift-off on MFL signal was analyzed by ANSYS and the design of the magnetizing system of MFL detector was optimized.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.