A Novel Planetary Gearbox Fault Diagnosis Method for Nuclear Circulating Water Pump With Class Imbalance and Data Distribution Shift

Abstract

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient samples and ideal operating environments. However, in practice, it is hard to acquire adequate fault samples from high-reliability equipment, thus inducing challenges in applying DL for intelligent diagnosis. Moreover, data distribution shift occurs due to working load variation or environment noise interference, severely degrading DL models’ performance. Aiming at the above problems, this article proposes a method called Q networks calibrated ensemble (QCE). Specifically, two improved deep Q networks are employed to enhance the attention to critical feature samples and fault samples by quantifying feature distribution and utilizing fault prior knowledge. Afterward, the calibrated Choquet integral is adopted to achieve the models’ ensemble, further enhancing the generalization ability and robustness of the diagnosis results. The proposed method has been verified on the nuclear circulating water pump (NCWP) test bench. Results indicate the superiority and reliability of the QCE method. In addition, results when working load changes demonstrate the outstanding generalization ability of the proposed method for cross-domain fault diagnosis with class imbalance. Furthermore, gradient-weighted class activation mapping (Grad-CAM) visualization verifies that the proposed method effectively learns fault features instead of noise.