- Tests show that AE signals may be detected from background noises in cutting process, a few hundreds millisecond before tool break down abrupt.
- An intelligent pattern classifier with B-P neural network is used in recognition of those five kinds of AE signals successfully.
- The AE signals from rolling bearing were processed by high-pass filter and demodulation and compared with vibration signals.
- The AE signals of tool conditions were decomposed using a recursive wavelet from which the features are extracted and delivered to an ART2 network for fault recognition.
- Moreover,the relation between AE signals and the failure processes of FWC from matrix cracks,delaminations,fiber breaks to burst with the water pressure increasing is analyzed.