Abstract: There is provided a diagnosis device of a joint of sheet metal, including: a detector that irradiates an ultrasonic wave to a diagnosis object including a first medium and a second medium having a joint, and senses a reflection signal reflected from the joint and a reflection signal reflected from the joint and thereafter, reflected toward the joint on an inner surface of the first medium; a delay material positioned between the detector and the diagnosis object to delay the reflection signal; and a diagnoser that calculates and displays a signal ratio by receiving the reflection signals sensed by the detector.

Abstract: 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.