1D-DDcGAN based signal fusion of real normal data and simulated fault data for the fault diagnosis of steel wire ropes without real fault data

Abstract The difficulty of obtaining sufficient fault samples is one of the vital problems in mechanical fault diagnosis with machine learning methods. Although fault data generated through simulations can supplement the fault samples, there exists a certain difference between the simulated data and the real ones, due to the lack of noise and other actual working environment factors in simulations. In engineering scenarios, since most equipment operates normally, the fault data is typically difficult to acquire, whereas the normal data is relatively easy to obtain and inherently contains valuable condition information absent in the simulated fault data. Therefore, the paper proposes a 1D-DDcGAN (1-Dimensional Dual Discriminator Conditional Generative Adversarial Network) based signal fusion method fusing the easily obtainable normal data and the simulated fault data to generate near-real fault data for the fault diagnosis of steel wire ropes without real fault data. Firstly, normal data of wire ropes are acquired through experiments and simulated data of different fault types are generated. Secondly, the 1D-DDcGAN is applied to fuse the real normal data and the simulated fault data to generate corrected-simulation fault data which is close to the real fault data. Finally, a convolutional neural network model is trained using these corrected-simulation fault data and tested using real fault data to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can fuse real normal data and simulated fault data to generate near-real fault data with closest similarity to real ones, and achieves the highest fault recognition accuracy among all comparative methods.