Robot Health Estimation through Unsupervised Anomaly Detection using Gaussian Mixture Models

Schnell, T.; Plasberg, C.; Puck, L.; Buettner, Timothee; Eichmann, Christian; Heppner, G.; Roennau, Arne; Dillmann, Rüdiger

doi:10.1109/case48305.2020.9217025

Cited by 8 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Methods to find anomalies in spatial, temporal and Spatio-temporal elements. Where S.1 [ 33 ], S.2 [ 34 ], S.3 [ 61 ], S.4 [ 35 ], S.5 [ 62 ], S.6 [ 26 ] represent spatial anomaly detection methods, ST.1 [ 42 ], ST.2 [ 43 ], ST.3 [ 44 ], ST.4 [ 46 ], ST.5 [ 47 ], ST.6 [ 45 ] represent Spatio-temporal anomaly detection methods and T.1 [ 38 ], T.2 [ 39 ], T.3 [ 40 ], T.4 [ 41 ] represent Temporal anomaly detection methods. The colour variation represents the year when the method was first used in Robotics for anomaly detection.…”

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

“…Moreover, the Gaussian mixture model (GMM) [ 67 ] is used for health monitoring and detecting anomalies from the sensors in the robots, wherein the healthy robot data is used for training the model that is then able to detect any anomalies in the time series data without any previous knowledge of the anomalies. Light Gradient Boosting Machine or LightGBM, a fast and high-performing gradient boosting algorithm, has recently been used for time series data analysis [ 39 ]. It is similar to the multiple linear regression (MLR) method used in time series forecasting in robotics systems [ 40 ].…”

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

“…Schnell et al [ 39 ] investigated robot anomalies by examining variations in time series data from different sensors using a Gaussian mixture model (GMM) framework. They analysed data from a robot joint when a payload was added, comparing the normal time-series pattern of the motor’s current values with the pattern during the load.…”

Section: Classification Of Anomalies In Armsmentioning

confidence: 99%

See 2 more Smart Citations

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

View full text Add to dashboard Cite

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

show abstract

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

Section: Classification Of Anomalies In Armsmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Figure 5.Methods to find anomalies in spatial, temporal and Spatio-temporal elements. Where S.1[33], S.2[34], S.3[61], S.4[35], S.5[62], S.6[26] represent spatial anomaly detection methods, ST.1[42], ST.2[43], ST.3[44], ST.4[46], ST.5[47], ST.6[45] represent Spatio-temporal anomaly detection methods and T.1[38], T.2[39], T.3[40], T.4[41] represent Temporal anomaly detection methods. The colour variation represents the year when the method was first used in Robotics for anomaly detection.…”

mentioning

confidence: 99%