A Consumption-Based Approach for Fault Diagnosis in Multi-DOF Robots

Abstract

Fault detection and diagnosis (FDI) in multi-degree-of-freedom (multi-DOF) robotic systems is essential for ensuring operational integrity in life-critical applications, such as robotic-assisted surgery and advanced bionics. Traditional methods often struggle with limited data sources and the masking effects of complex motion dynamics on fault localization. The theoretical innovation of this work lies in a novel, hierarchical FDI architecture that synergistically integrates frequency-domain signature modeling with bidirectional temporal learning to decouple motion-induced power fluctuations from subtle fault signals. We utilize the Bode Equation Vector Fitting (BEVF) method to precisely model non-stationary dynamic fault signatures, providing a high-fidelity reference baseline. A two-stage classifier is then employed: a Bidirectional Long Short-Term Memory (BiLSTM) network first localizes faults to a specific joint with 94.4% accuracy by exploiting bidirectional temporal dependencies in the power residuals. Subsequently, a Support Vector Machine (SVM) diagnoses the fault type (mechanical or electrical) with an overall accuracy of 76.3%. This approach successfully identifies high-impact electrical faults while capturing subtle mechanical deviations often masked by the robot’s internal compensatory control loop. Our framework demonstrates a robust and non-invasive solution for FDI, significantly improving diagnostic granularity and providing actionable insights for high-reliability robotic systems.