Trainable Pseudo-Label Generator With Reinforcement Learning for Industrial Fault Diagnosis

Although the data collected by modern industrial processes is increasing, it is quite difficult to obtain sufficient real fault labels. Therefore, semi-supervised fault diagnosis has become one of the problems of great practical significance in data-driven industrial process monitoring. To handle the semi-supervised fault diagnosis issue, this work proposes a novel general trainable pseudo-label generator (TPG) on the basis of deep reinforcement learning. In TPG, the generator of TPG is trainable, and the parameters of the generator are optimized through the feedback performance of the validation test. The iterative process of the pseudo-label generator can be formalized as a sequential decision process. The non-differentiable optimization issue of generating pseudo-labels based on feedback can be solved by reinforcement learning. As a data-level algorithm, the pseudo-label generator can be simply used in conjunction with the existing supervised diagnosis model. The practicability and superiority of TPG are demonstrated through one synthetic dataset and two industrial processes.