Study On Condition Monitoring Of Pressure Vessel Based On Extracting Effective Features Of Acoustic Emission Digital Signal

In recent years, acoustic emission (AE) technique has received more and more attention for its characters of real-time monitoring and global detecting in pressure vessel nondestructive test. Parameter analysis and waveform analysis are two main methods of acoustic emission signal processing. Most methods proposed in literature are based on the AE signals received from the measurement device, with no specific consideration of signal source. When AE signals are recorded, it contains the information from its source, propagation medium, coupled stiffness and AE sensor. So there is no one to one correspondence between the acoustic emission analysis result and the damage information of pressure vessel. In this thesis, a new characteristic parameter is proposed, by whom the change of source can be reacted directly.The deformation degree of metal materials has been widely used to assess the condition of pressure vessel. Q235steels with different propagation distances, geometry structures, coupled stiffness and AE sensor are stretched to get the corresponding acoustic emission signals in this paper. The main contents and results of this work are as follows: 1) The filtering method of AE signal is selected. The obtained AE signals contain signals from metal deformation, friction signals and environmental noise. Only signals from metal deformation and friction signals are interested in this paper. Most of them exist in a high-frequency range. And AE signals from the deformation of metals are usually nonlinear and non-stationary. Compared with WT, it is easy to obtain a satisfactory resolution using EMD analysis at the high-frequency range.2) A new characteristic parameter is proposed. There is no one to one correspondence between obtained AE signal and AE source. Both signals from metal deformation and friction signals contain the information from their sources, propagation medium, coupled stiffness and AE sensor. And the influence of propagation medium, coupled stiffness and sensor are equally. As there is no Kaiser effect in friction signals, the total signal energy of produced friction signals shall keep unchanged with the process of metal deformation. The ratio of signal energy at tensile frequency range to signal energy at frictional frequency range, is proposed as the AE characteristic parameter.3) The performance of proposed characteristic parameter to determine the deformation degree of metals is tested in this work. Specimens made of Q235plain carbon steels with two different geometry structures were chosen for tensile test in this work. Different propagation distances of elastic waves by the distance between fracture and the location of sensor, different coupled stiffness and different sensor are adjusted. Results show that different deformation stages of metal materials can be identified not influenced by propagation distance, geometry structure, coupled stiffness and AE sensor.With ratio of signal energy, real-time on-line condition monitoring and assessment of pressure vessels can be achieved based on AE signal analysis.