FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 7; pp. 4961 - 4971
• Main Authors: Gao, Yun, Liu, Xiaoyang, Xiang, Jiawei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Bearings; Computer simulation; Datasets; detection; Fault detection; fault samples; Finite element method; finite element method (FEM); Generative adversarial networks; Mechanical systems; Simulation
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.2968370

Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault detection scheme is proposed to build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation is used to simulate samples with different faults to overcome the shortcoming of missing fault samples. Second, to enlarge datasets, new samples similar to the simulation and measurement fault samples are generated by generative adversarial networks and further combined with the original simulation and measurement samples to obtain synthetic samples. Finally, the synthetic and unknown fault samples are severed as the training and test samples, respectively, to the classifiers of AI models, and the unknown fault types will be finally determined. A public datasets of bearings have been used to verify the effectiveness of the proposed scheme. It is expected that the proposed scheme can be extended to complex mechanical systems.