Intelligent Fault Quantitative Identification via the Improved Deep Deterministic Policy Gradient (DDPG) Algorithm Accompanied With Imbalanced Sample

Abstract

The imbalanced amount of faulty and normal samples seriously affects the performance of intelligent fault diagnosis models. Aiming to solve the above problem, an improved deep deterministic policy gradient algorithm (ResDPG) based on actor-critic architecture is proposed. In ResDPG, a multi-channel time-frequency representation (TFR) is obtained by the synchrosqueezed wavelet transform (SWT) to avoid the non-stationary of the original signal. ResNet is introduced to construct an actor network for extracting representative deep fault features to improve the accuracy of fault diagnosis, while AlexNet is utilized to build a critic network and guide the actor to train in the right direction according to the evaluation mechanism. The model constructs a rational and practical reward function based on the imbalance ratios and uses the minimum distance between the centers of the classes as the feedback of the reward. The optimized state transfer function improves the learning frequency of minority classes. Verified via two datasets of rolling bearing, ResDPG can independently and autonomously achieve accurate fault quantitative identification with high efficiency, stability, and generalization. It also achieves state-of-the-art performance under unbalanced data and variable load.