In order to solve the problem of low remaining useful life (RUL) accuracy of rolling bearings under small samples and noise interference

a RUL prediction method of rolling bearings using information least squares generative adversarial network (InfoLSGAN) and actor-critic (AC) algorithm was proposed. Stacked denoising autoencoder

information generative adversarial network and least squares generative adversarial network were integrated to construct InfoLSGAN

which can automatically extract interpretable and robust features from noisy data

and solve the problem of vanishing gradients. The training algorithm based on AC was utilized to train the InfoLSGAN to reduce the training time and accelerate the convergence. According to the InfoLSGAN after training

a softmax classifier was used to predict the rolling bearing RUL in test samples. The effectiveness of the proposed method was validated through an accelerated fatigue life experiment of rolling bearings. The experimental results demonstrated that when the signal-to-noise ratio was equal to 0

the proposed method increased the RUL prediction accuracy of rolling bearing test samples by at least 10%. In the case of small samples

the average accuracy of the RUL prediction of rolling bearings was 9584%.