The invention discloses a rolling bearing fault diagnosis method based on a two-way
memory cycle neural network. An existing rolling bearing fault diagnosis method does not consider a single logical structure characteristic of data after characteristic extraction and a fault type can not be integrally determined from the data when fault data is processed. Aiming at the above defects, the method of the invention comprises the following steps of firstly, acquiring a program data sample, carrying out standardized preprocessing on
vibration acceleration data, making the collected data accord with standard normal distribution, and then using a time-
frequency domain characteristic
extraction algorithm to obtain 512 time-
frequency domain characteristic vectors; then, constructing an improved two-way
memory type cycle neural network fault diagnosis model, using an idea of a simple design, and then using sample data to
train a neural network weight parameter, after iteration training, generating a model which can map a relationship between bearing data and a fault type, wherein the designed memory-type cycle neural network includes a forgetting gate, an input gate and a cellular state; and finally, using the model to carry out
fault analysis so as to achieve accurate diagnosis of a rolling bearing fault.