Feature extraction of machinery diagnosis using neural network

Vibration monitoring of machinery involves the collection of vibration data from machine components and detailed analysis to extract features that reflect the running state of the machinery. The machinery state can be described accurately if the feature is correctly selected. A new approach is developed in this paper, in which the optimization feature subset is extracted, making full use of the information processing ability of neural networks, and using sensitivity of the feature parameter as the criterion of selection. In this approach, feature parameter sensitivity and feature parameter consistency are appraised simultaneously when accomplishing the training of neural networks. In addition, combining with a logical rule, an optimization feature subset is obtained. This method solves the problem of the conventional inefficient way where the optimization feature subset is extracted from many feature parameter types of vibration signals. The new feature subset of the reduced dimensions provides accurate data for the precision analysis. As a result, the accuracy of the automonitoring system can be improved