Adaptive Gaussian Mixture Models-based Anomaly Detection for under-constrained Cable-Driven Parallel Robots

2025-07-10

AI Summary

For under-constrained cable-driven parallel robots (CDPRs), external disturbances—such as gust winds or cable impacts—during point-to-point positioning may induce pose instability or cable tension loss, posing critical safety risks. To address this, we propose an unsupervised, real-time anomaly detection method relying solely on motor torque signals. Our core contribution is an adaptive Gaussian Mixture Model (GMM) that integrates Mahalanobis distance-based anomaly scoring with dynamically adjusted statistical thresholds, updated online via a sliding-window mechanism to enhance robustness against operational drift and environmental variability. Evaluated over 14 long-duration experimental trials, the method achieves a 100% true positive rate, an average true negative rate of 95.4%, and a mean detection latency of only 1 second—outperforming both conventional power-thresholding and non-adaptive GMM approaches.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Cable-Driven Parallel Robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive, unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period, just a few seconds, during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation.

Problem

• Detect anomalies in CDPRs using only motor torque data
• Develop adaptive GMM-based algorithm for real-time anomaly detection
• Validate method under varying wind conditions for reliability

Innovation

• Adaptive Gaussian Mixture Models for anomaly detection
• Unsupervised outlier detection using torque signals
• Real-time Mahalanobis distance evaluation with updates