Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery

For the fault diagnosis problems of rotating machinery in the real industrial practice, measurement data with imbalanced class distributions negatively affect the diagnostic performance of most conventional machine learning classification algorithms since equal cost weights are assigned to different fault classes. Meanwhile, the widely used traditional data generation methods for the imbalanced data problem are limited by data dependencies over time continuity. To fill this research gap, this paper develops a new diagnostic framework based on the adversarial neural networks (GAN) and multi-sensor data fusion technique to generate new synthetic data for data compensation purpose. Two different practice modes are designed based on this framework according to the position logic of the data fusion, namely a Pre-fusion GAN mode and a Post-fusion GAN mode. More concretely, without data pre-processing, the designed generator generates synthetic data to puzzle the discriminator and the synthetic data that out-trick the discriminator can be used to compensate the minor class. To avoid data dependency and to ensure the generality of the proposed framework, the network modelling are trained with a more practical approach where the training and test data are obtained under different rotating speeds. Two imbalanced data sets on the rotating machinery, one benchmark public rolling bearing data set and another gear box data set acquired in our lab, are used to validate the proposed method. The performance is examined through a wide range of data imbalanced ratios (as high as 30:1), and compared with other state-of-the-art methods. The experiment results conclude that the proposed Pre-fusion GAN and Post-fusion GAN frameworks both have good performance on the imbalanced fault diagnosis of rotating machinery.