<p>Industrial robots, widely employed to boost production efficiency, encounter escalating risks of joint faults as their service time lengthens. However, end-effector motion anomalies may stem from faults in the end-effector itself or from motion propagation in other joints. Moreover, the scarcity of fault samples for detection poses significant challenges. Install extra accelerometers for more precise fault diagnosis might increase the system’s complexity and costs. To tackle these challenges, this study leverages the ease of data acquisition to analyze current data from multi-joint industrial robots. A hybrid learning method is proposed for cross-device fault detection to identify the defective joint. This method integrates features from deep networks and spectral analysis to harness knowledge from both other robots and the target robot. An unsupervised model is used to assess the status of the joints based on the fused features. The proposed method’s effectiveness is validated through ablation studies and method comparisons. Results demonstrate that it accurately detects the abnormal joints without misjudgment.</p>