Reliability analysis using experimental statistical methods and AIS: application in continuous flow tubes of gaseous medium

Abstract

The motivation for the development of this work arose from the observation of maintenance in pressure vessels, which are categorized as highly hazardous security risk products. The costs of detecting failures in the production systems allow the result of the process to be safe and of good quality, using standardized tests internally within the company. The main objective of this work demonstrates the efficiency and robustness of the artificial immune system (AIS) of negative selection in the detection of failures by recognizing the vibration signals and categorizing them in the degree of probability and level of severity of failures. The intrinsic objectives are the application of the elimination of signal noise by the Wiener filter, and the processing of data-Wiener data using experimental statistics. The result of this work successfully demonstrates the precision between the experimental statistical and AIS techniques of negative selection; the robustness of the algorithm in precision and signal recognition; and the classification of the degree of severity and probability of failure