A Threshold-Control Generative Adversarial Network Method for Intelligent Fault Diagnosis

Fault diagnosis plays the increasingly vital role to guarantee the machine reliability in the industrial enterprise. Among all the solutions, deep learning (DL) methods have achieved more popularity for their feature extraction ability from the raw historical data. However, the performance of DL relies on the huge amount of labeled data, as it is costly to obtain in the real world as the labeling process for data is usually tagged by hand. To obtain the good performance with limited labeled data, this research proposes a threshold-control generative adversarial network (TCGAN) method. Firstly, the 1D vibration signals are processed to be converted into 2D images, which are used as the input of TCGAN. Secondly, TCGAN would generate pseudo data which have the similar distribution with the limited labeled data. With pseudo data generation, the training dataset can be enlarged and the increase on the labeled data could further promote the performance of TCGAN on fault diagnosis. Thirdly, to mitigate the instability of the generated data, a threshold-control is presented to adjust the relationship between discriminator and generator dynamically and automatically. The proposed TCGAN is validated on the datasets from Case Western Reserve University and Self-Priming Centrifugal Pump. The prediction accuracies with limited labeled data have reached to 99.96% and 99.898%, which are even better than other methods tested under the whole labeled datasets.