Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data

Abstract

Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems. Nevertheless, variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis. Taking this into account, this study presents a new intelligent fault diagnosis (IFD) approach for planetary gearbox using a transferable deep Q network (TDQN) that merges deep reinforcement learning (DRL) and transfer learning (TL). First, a DRL environment simulation is designed by a predefined classification Markov decision process (CMDP). Then, leveraging varied-size convolutions and residual learning, a multiscale residual convolutional neural network (MRCNN) agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation. Next, a large source dataset is obtained from complex conditions, and this agent learns an IFD policy via autonomous interaction with the data environment. Finally, a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data, which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks. The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%, but also in two target tasks with 99.63% and 98.37%, respectively.

Conflict of Interest Statement

The authors declare no conflicts of interest.