Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network

At present, data acquisition and fault diagnosis based on a variety of sensors have become an indispensable means for manufacturing enterprises. However, through the independent analysis of all kinds of sensor data, the traditional analysis method fails to make full use of the interrelationship between data sources.

A new feature fusion approach that is based on Convolutional Neural Network (CNN) is put forward in this study for rotating machinery fault diagnosis. For multi-source data, some data sources are extracted with empirical features and others are extracted with hidden features. CNN is adopted to obtain the recessive features of complex signal waveform, such as acceleration, displacement, etc.