Fault Diagnosis Methods in Industrial Robotic Systems

Fault diagnosis in industrial robotic systems encompasses a suite of techniques aimed at detecting, isolating and characterising anomalies in robotic components before they lead to unplanned downtime or safety incidents. Traditional model-based approaches rely on first-principles mathematical models of robot kinematics and dynamics, comparing measured signals such as motor currents, joint torques or power consumption against expected patterns. In contrast, data-driven strategies exploit machine learning and deep learning to learn normal and faulty behaviours directly from sensor streams, including vibration, electrical current, acoustic emission and encoder data. Hybrid methods integrate physical knowledge with data-driven models to compensate for scarce fault data or nonlinearities in robot dynamics. Recent developments in physics-based digital twin simulations enrich prognostics by fusing degradation curves with real-time data, enabling estimation of Remaining Useful Life. Advances in transfer learning and unsupervised anomaly detection allow fault-tolerant systems to adapt across varying loads, speeds and tasks. Together, these methods support condition-based and predictive maintenance regimes that enhance productivity, reduce lifecycle costs and ensure global supply-chain resilience.

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Fault Diagnosis Methods in Industrial Robotic Systems publication trend

The graph below shows the total number of articles in fault diagnosis methods in industrial robotic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Anomaly detection: The process of identifying data patterns that diverge from an established normal behaviour in sensor or process data.

Digital twin: A virtual replica of a physical system that integrates real-time data and physics-based models to simulate performance and predict failures.

Transfer learning: A technique where a model trained on one task is adapted to a related task, reducing data requirements and improving generalisation.

Prognostics and health management (PHM): A discipline combining diagnostics and remaining life prediction to support condition-based and predictive maintenance.

Scalogram: A time–frequency representation of a signal obtained via wavelet transform, used as input for image-based deep learning models.

References

1. Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: a data-driven approach. Journal of Computational Design and Engineering (2022).
2. Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots. Robotics and Computer-Integrated Manufacturing (2021).
3. Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review. Mathematics (2023).
4. Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System. Mathematics (2023).