A multi-fault diagnosis method for rolling bearings

In conditions of multi-fault coupling, varying loads and speeds, as well as noise interference, bearing vibration signals present various complex issues, leading to difficulties in feature extraction and the need for a large number of training samples for diagnostic methods. This paper designs a multi-fault coupling experiment for rolling bearings under varying load and speed conditions and proposes a new fault diagnosis method that uses the power spectrum of the AR model and a convolutional neural network to diagnose complex multi-faults in rolling bearings. It takes the original vibration signal as input, uses the AR model to convert the time-domain signal into a power spectrum, and then classifies it using a convolutional neural network. To test the performance of the AR model power spectrum convolutional neural network, this method was compared with some fault diagnosis methods. The results show that this method can achieve higher diagnostic accuracy under varying loads and speeds, and requires fewer training samples. In addition, the noise resistance of this method is also superior to other fault diagnosis methods.