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

Abstract

The fast and efficient fault diagnosis is the key to guarantee uninterrupted working of facilities, which is more frugal and trustworthy than scheduled upkeep. 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. The fusion of statistical features and recessive features is a new set of features and is input into Light Gradient Boosting Machine (LightGBM) model. The stator and rotor fault experiment is designed and implemented to verify the advantages of the proposed method. Compared with the traditional approaches, this method is 3% more accurate or at least 4 times faster than the traditional method under the same conditions.