Papers by Keyword: Weld Flaw

Abstract: Pressure steel pipings in a water power plant have been in service for over fifty years. In order to assess the safety of the piping, whole check and tests for the piping were carried out. The material for some piping is ST3 from former USSR, the other is A3 made in China. The mechanical properties were investigated by tests and were compared with the original material properties. The result shows the yield strength and tensile strength of serviced steels are less than those of original materials. The surfaces of the piping were checked to investigate the corrosion, surface defects. The flaws in the welds and near the welds of the pipings were detected by UT and MT, and several weld flaws were found. Based on the test and NDT investigation results, the strength, fracture and fatigue life of the pipings are assessed according to Chinese standard GB/T19624-2004 and British standard BS7910-2000. The assessment results show that the strength of the piping is enough, and the pipings with these flaws does not fracture. The piping can be safe in service under the normal operating condition for 104 to 106 cycles.

Abstract: In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to signal processing code and pattern classifying code by user made programming code. For evaluation of flaw signal is reflected on welding flaw, user-made program codes are composed of signal processing and probability neural network (PNN) and backpropagation neural network (BPNN). And then, we actually confirmed to the theoretical advantage of each neural network method compared probability neural network with backpropagation neural network for classification and recognition rate. For the application of classifier to SWP inspection system, BPNN classifier is adequate in the first stage. And then, the application of PNN classifier is adequate as the next stage. Because of PNN application need enough sample data that is due to probabilistic density function.