Single Source Domain Generalization Fault Diagnosis Method for Reducers Based on Causal Learning

Objectives Existing neural network-based fault diagnosis methods usually assume that training and testing data follow the same distribution. However, ship reducers often operate under variable speed, load, vibration, and environmental conditions, which can cause significant distribution shifts in vibration signals and reduce diagnostic accuracy under unseen working conditions. Domain adaptation methods can mitigate this issue but usually require target domain data, which are difficult to collect in practical ship operation. To overcome these challenges, this study proposes a causal learning-based single source domain generalization method, named CLSSDG, for fault diagnosis of ship reducers. The proposed method is designed to improve model generalization under unknown operating conditions by using only one source domain, thereby reducing data collection requirements and enhancing practical applicability.

Methods First, a data transformation module is developed to generate auxiliary domain samples from source domain data using five transformations, namely noise addition, time stretching, amplitude scaling, slice rearrangement, and zero-value masking. These transformations simulate signal variations caused by interference, speed and load fluctuations, and data loss. Second, a causal graph is constructed to describe the relationships among input data, transformation factors, semantic features, and fault labels. Semantic features represent intrinsic fault-related information, while transformation factors capture condition-related variations. Since observed signals are influenced by both types of factors, models may learn interference features rather than stable fault features. To address this problem, a counterfactual inference module is designed to estimate the causal effects of different transformation factors on diagnostic predictions and identify the main contributors to domain shifts. Third, a domain alignment module corrects auxiliary features according to the estimated causal effects. Feature mappings are assigned to different transformation factors and fused through a weight mapping network. By minimizing the distance between source features and corrected auxiliary features, the model learns robust and transferable representations.

Results Experiments are conducted on a self-built reducer test bench consisting of a driving motor, loading motor, two-stage reducer gearbox, rotor, and vibration sensors. Six health states are considered, including normal state, gear broken tooth, gear pitting, bearing outer race wear, gear pitting coupled with bearing outer race wear, and gear broken tooth coupled with bearing outer race wear. Data are collected under three speeds, namely 600 r/min, 800 r/min, and 1000 r/min, and six cross-condition diagnostic tasks are designed. The experimental results show that CLSSDG achieves an average diagnostic accuracy of 90.34% without using any target domain samples, outperforming existing single source domain generalization methods. Compared with domain adaptation methods requiring target data, CLSSDG achieves comparable or better performance, demonstrating strong practical value. Ablation studies confirm the necessity of the data transformation, counterfactual inference, and domain alignment modules. Direct domain alignment without causal guidance may lead to negative transfer, whereas causal-guided alignment improves generalization. Confusion matrix analysis shows that CLSSDG provides balanced recognition across all fault categories.

Conclusions The proposed CLSSDG method offers an effective and interpretable solution for reducer fault diagnosis under unseen operating conditions. Its main contribution lies in integrating causal learning into single source domain generalization, where counterfactual inference is used to analyze the causes of domain shifts and guide feature correction. This strategy reduces the limitations of conventional data augmentation and improves cross-condition diagnostic robustness. Since the method requires only one source domain and no target domain samples, it is suitable for practical ship equipment monitoring. This study provides technical support for intelligent fault diagnosis, predictive maintenance, and health management of ship power transmission systems and contributes to the development of reliable smart ships.