Simulation-Generated Data for Fault Detection and Diagnosis in Mobile Robots

Time-Series Anomaly Detection for Mobile Robots in Automotive Active Safety Testing using an RNN-VAE

Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present hardware defects. This circumstance can lead to more severe failures, causing expensive repairs and operational downtime. This work proposes, for the first time, a reconstructionbased time-series anomaly detection model for these mobile robots, considering defect classes such as unevenly worn full-rubber tires or damaged dampers. Unlike prior publications, the proposed approach leverages the vast quantities of unlabeled data generated during routine operation through a simple pre-training step. Furthermore, it optimizes the hyperparameters of the implemented gated recurrent unit-based variational autoencoder (GRU-VAE) and evaluates both a stateless, windowed training approach and one using truncated backpropagation through time (TBPTT). The model's generalization capabilities are demonstrated by successfully detecting six defect types, with four of them not present in the data used for hyperparameter optimization and threshold selection. This is validated using a test set collected from five system instances at various points over a period of several months, achieving an F1 score of 0.936, indicating strong practical viability.

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

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.

Unsupervised Learning for Industrial Robot Health Monitoring: Trends, Techniques, and Challenges

A thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots explores significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation.

Intelligent Sensor Fault Diagnosis in Autonomous Robotic Platforms

Autonomous robotic platforms have become an integral part of industrial automation, intelligent manufacturing, autonomous transportation, precision agriculture, healthcare robotics, and hazardous environment exploration. The operational reliability of these robotic systems strongly depends on the accuracy and health of their sensing infrastructure. Sensors including inertial measurement units (IMUs), LiDAR, cameras, ultrasonic sensors, GPS modules, encoders, force-torque sensors, and proximity sensors continuously provide environmental and operational information for autonomous decision-making. However, sensor degradation, calibration drift, communication failures, environmental interference, and hardware aging can significantly deteriorate robotic performance and may even lead to catastrophic failures. Consequently, intelligent sensor fault diagnosis has emerged as a critical research area that combines artificial intelligence, machine learning, data analytics, and model-based reasoning to identify, classify, and predict sensor faults in real time. In this paper, a complete intelligent sensor fault diagnosis framework for autonomous robotic platforms is proposed. The proposed framework utilizes the integration of multi-sensor data fusion, feature extraction, deep learning-based fault classification, anomaly detection, and predictive maintenance components into a unified architecture. Experimental performance evaluation shows significant enhancements versus traditional threshold-based diagnostic approaches in terms of high fault detection accuracy, lower false alarms and reduction in the latency for diagnosis among different faults with an increased reliability level on system diagnosis. These results demonstrate that AI-assisted diagnostic models significantly increased robotic autonomy through the ability to proactively manage faults, reduce downtime and enhance operational safety. The proposed framework is suitable for deployment in Industry 5.0 manufacturing systems, autonomous vehicles, collaborative robots, and intelligent service robotics.

Multiple robots (robotic centers) and systems. Remote sensing and non-destructive testing

Bootstrapping Self-Supervised Learning of Binary Classification Using Error Bounds: A Case Study on a Robotic Insertion Task

A data engine which gathers data and improves its performance while executing the task, and demonstrates the ability to learn and reduce the need for expensive verifications over time, while staying within the set error-rate.