ACOUSTIC EMISSION DIAGNOSIS DEVICE FOR GAS VESSEL USING PROBABILISTIC NEURAL NETWORK AND METHOD OF DIAGNOSING DEFECT OF CYLINDER USING THE SAME

摘要

An acoustic emission diagnosis device is provided for a gas vessel using a probabilistic neural network, and a method of diagnosing a defect of the gas vessel using the same, in which acoustic emission signal sensors are attached to multiple portions of the gas vessel. Acoustic emission signals are detected when filling the inside of the gas vessel with gas, when holding the pressure after filling, and when decreasing the pressure. Features in which the detected acoustic emission signals are varied are extracted, and a damaged degree of the gas vessel is determined using the probabilistic neural network that has been trained through a classification learning algorithm for the extracted features.

主权项

1. An acoustic emission diagnosis device for a gas vessel using a probabilistic neural network, the device diagnosing a defect of the gas vessel including a metal liner and a composite material wound at a part of an outer surface of the metal liner to reinforce the metal liner, the device comprising:
a second acoustic emission signal sensor that is attached to a center of an outer surface of the composite material to detect an acoustic emission signal; a first acoustic emission signal sensor that is attached to a bottom part of the outer surface of the metal liner at which the composite material is not provided to detect an acoustic emission signal; a signal processing unit that represents the acoustic emission signals detected by the first acoustic emission signal sensor and the second acoustic emission signal sensor as two or more acoustic emission parameters among the number of hits, amplitude, energy, rise time, count, duration, strength, a signal level, and RMS (Root Mean Square); and a diagnosis unit that extracts features by analyzing the acoustic emission parameters and determines a damaged degree of the gas vessel using the probabilistic neural network that has been trained through a classification learning algorithm for the acoustic emission parameters.