A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity

Because the fault characteristic frequencies of a rolling bearing are submerged in strong noise when it fails early, the fault feature in the original signal is relatively weak to allow the diagnosis of the bearing. Consequently, the method to extract a weak fault feature is becoming a challenging research topic in fault diagnosis. Traditional diagnostic networks are typically trained by the time series or the frequency spectrum of the acquired discrete signal fragment, whereas the connection of the local fragments (quasi-periodicity) is neglected, resulting in low diagnostic accuracy for the bearing under strong noise conditions. To solve this problem, a novel weak fault feature extraction and diagnosis method, composed of two parts, is proposed in this paper. The first part is a multi-channel continuous wavelet transform (MCCWT), by which the original temporal signals can be more easily transformed into a new representation with several channels and fewer network parameter requirements than those required by the traditional methods. The second part is a convolution-feature-based recurrent neural network (CFRNN) that is based on a traditional recurrent neural network (RNN). In the latter, a recurrent unit combining several residual blocks and a long short term memory (LSTM) block is proposed to mine the temporal features and the local vibration characteristics simultaneously. The efficiency of the proposed diagnosis method is validated respectively by the datasets collected by simulating fault bearings with strong noise and using real fault bearings containing faults at an early stage.