MFLThe magnetic flux leakage (MFL) technique is generally considered to be an efficient and effective non-destructive testing (NDT) method and has been widely used in the oil field inspection industry for the examination of drill pipe. This paper studies the resistance distribution in the magnetic circuit of magnetic flux leakage detection probes and proposes the method of adding a pole piece to reduce magnetic resistance. The magnetic circuit model is simulated by using finite element analysis and the results show that the detection probe with a pole piece can strengthen the magnetic leakage signal for a drill pipe defect, and pole pieces with different thicknesses have little influence on the defect magnetic leakage signal in the circuit. Practical testing confirms that the probe with a pole piece can clearly improve the SNR of the MFL signal. The analysis results are of great value for improving defect identification rates and for the precise detection of defects.This book will provide guidance on the subject of Infrared Thermography (IRT), namely heat transfer theory, equipment selection, applications and operational factors; and as such can be used as an information source to support IRT operations. Volume 2: Applications by A N NowickiThis book is aimed at thermographers who have attained at least Level 1 accreditation in IRT. It is recommended that the reader also has some operational experience in order to have the required level of knowledge to understand the terminology and diagnostic descriptions included.
The Electroencephalogram signal which is picked up by electrodes from the skull of the patient's body is effected severely by noise of power line, noise of human body muscles, noise of human lungs and noise of the baseline. The baseline noises arise even because of patients body movements and breathing, the sensors are loosely connected and eye movements. Researchers have applied many algorithms for removal of these noises. The basic important algorithms used are Kalman filter, Moving average and Cubic spline. The Electroencephalogram signals are highly contaminated with various artifacts both from subject and from equipment interferences. For efficient detection of tumor artifacts exist in the electroencephalogram signal are removed using analogue filtering. In this research Fast Independent Component Analysis algorithm is used to separate the noise and get the features which are buried in the extended band of noise. For problem solution a unique Fast Independent Component Analysis filter is being proposed in this research.
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