Recognition Of Metal Crack Acoustic Emission Signal And Research On Warning Method

Under the alternate loading, accruing flaw in metal structure is one of faults that happen in metal structure frequently, especially in large-scale complex structural parts such as Francis turbine etc. As the effect of alternate loading, the flaw in the metal structure could develop continuously, and at the beginning, the fatigue flaw is very thin, therefore in this case, detecting flaw seems to be very necessary. Acoustic emission (AE) testing is applied widely as a rising multidisciplinary nondestructive testing technology in equipment of nondestructive examination and on-line monitoring. In this article, based on a great deal of resources from home and foreign country, AE technique will be applied to metal fatigue crack's on-line monitoring.Since AE signal's transient and randomness, it is composed of a series of rich frequency and pattern, and poor working environment and many acoustic emission source categories at the scene. It is very difficult to gain comparatively pure metal fatigue AE signal by simple methods such as frequency or amplitude filtering.After analyzing the actual work environment of the runner of Francis turbine, many AE signals can be determined initially at the scene, such as metal fatigue crack AE signals, cavitations, fricative AE signals, and increase the standard lead off signals.Summary of analysis method of AE and 13 characteristic parameters; Feature extraction was designed by use of neural networks and pattern recognition method, and five characteristic parameters that can express AE most were filtered; At the same time, the most notable feature parameters on classification were obtained by use of the separability criterion, to verify the accuracy of feature extraction; According to the experimental results, metal crack AE signals can be identified with the five characteristic parameters.In addition, in the large and complex component of the AE testing, multi-sensor detection system was used frequently. It can be fused in data layer by the independent component analysis, and decision-making layer by D-S evidence theory, to integrate multi-sensor signals, reduce uncertainty of recognition, improve the capabilities of system's identification, fault-tolerance and anti-jamming.Based on the analysis of neural networks and pattern recognition referred, when metal crack happened, alarm can be carried out in virtue of the data fusion technology and the warning theory.