Definition of the defect type and sizes is the most important problem of the ultrasonic testing industrial equipment in operation. Information about the defect type, sizes and coordinates will allow to make valid conclusion about danger level of such reflector for object operation. One of the solutions of this task is imaging of all border of the reflector. It is possible to use two phased arrays located from the different sides of the control area. For the phased array located to the left side of the reflector (N-channel) it is possible to receive a set of partial images of the reflector accounting wave type transformation at the repeated reflections of the ultrasonic impulse (the multimode regime). The same set of images could be received from the echo signals measured by the phased array located to the right side of the reflector (P-channel) and from the signals transmitted and received by different phased arrays (NP-channel). Integration of partial images receiving from all channels by the large quantity of acoustic schemes allows to receive the image of all defect border and to make a valid conclusion about its type. This approach allows to get information about defect type when access is only one side with smaller efficiency. To improve the image quality, the measured echo signals can be subjected to the procedure of extrapolation its spectrums for reduction the impulses length.

Proposal of a method for identifying the high-level risks in the Magnetic Particle Inspection (MPI) of ferromagnetic material parts, based on Analytic Hierarchy Process (AHP) and Bayesian Belief Network (BBN). The combination of probability and the impact identified the most significant risks, which needed to be addressed to improve quality management system and ensure organization sustainability. The inspection of critical ferromagnetic parts with Magnetic Particle in the manufacturing and services industry is very critical. The correct selection and use of an adequate analysis method to ensure inspection process reliability is very important and can avoid part failure and costly accidents. As a methodological approach, the estimated risk probabilities for the risk factors are loaded into Bayesian Belief Networks software to assess the probability of occurrence of undesirable events and AHP is utilized to rank the relative importance (effect) of risks. The combination of probabilities and the effects identified the most significant risks. No evidence of previous work could be found about the use of AHP and BBN on the risk assessment of MPI of critical hardware. As far as the authors are aware, this is the first time this method is being used in this specific process. The novelty of the paper is the combination of Bayesian Belief Networks with AHP to select the most significant risk in the inspection of critical parts. The application of the method revealed that the most significant risks in the inspection of critical hardware are related to operator failure, unfavourable control and environment, negative organizational factors. The paper proposes responses to these risks aiming at preventing the occurrence of failure in the MPI inspection of critical hardware. This paper contributes to the literature in the field non-destructive inspection of critical parts. The proposed model has also practical implications and is an invaluable source for non-destructive inspection professionals, safety engineers, quality managers and decision makers in companies to augment their information and to identify critical risks in the non-destructive inspection of critical ferromagnetic parts. The identification and prioritization of risk factor makes it easier to allocate resources to prevent critical parts failure and improve product quality and ensure organization sustainability.