Fuzzy soft morphology self-adaptive edge detection of parameter image for rotating machinery

Aiming at the problem that the edge features of vibration parameter images for rotating machinery are difficult to be extracted, the filtering enhancement processing method and the self-adaptive edge detection operator are established according to fuzzy soft morphology theory. The 3d vibration parameter images of rotor's normal state, fault of unbalance, misalignment, steam exciting vibration and bearing pedestal looseness were obtained from the experiments on the modeling of 600 MW turbine rotor experimental bench. These 3d images were transformed to 2d gray-scale images, and these 2d gray-scale images are processed with fuzzy soft morphology filtering enhancement processing and self-adaptive edge detection. The results show that with this method the noise of parameter images can be filtered out and the edge features of images can be extracted effectively, so that a new method to extract image features for rotating machinery fault diagnosis is provided.