A comparative study of different algorithms using contrived failure data to detect robot anomalies

Wescoat, Ethan; Kerner, Scott; Mears, Laine

doi:10.1016/j.procs.2022.01.265

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Anomalies in the kinematic or dynamic behaviour of a robot can be detected by comparing the observed motion with the expected motion based on the system’s kinematic or dynamic models [ 50 ]. Deviations from the expected behaviour can indicate the presence of an anomaly [ 51 ].…”

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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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.

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Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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“…As relevant remarks from the eligible documents, it is suggested by Wescoat et al, (2022) that the main limitation of AI-based approaches for FDD of cobots is the limited availability of failure data. Hence, laboratory applications with simulated failures (overloads and excessive friction) are Objective and description (Wescoat et al, 2022) The objective of the work is to compare three different machine learning algorithms to characterise the overall health state of a 6-axis collaborative robots. The authors compared Random Forest Regression, Support Vector Regression and a Deep Neural Network Regression, where the anomalous condition was the overload of the end-effector.…”

Section: Concluding Remarks From the Literature Reviewmentioning

confidence: 99%

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

2023

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Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor.

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“…Therefore, for diagnostics, each of the methods presented has its own scope of application and is used to achieve various goals [1,6,7]. The mathematical methods are a powerful tool for evaluating and analyzing data, thereby identifying anomalies in the behavior of the mechanism.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

Autsou,

Kudelina,

Vaimann

et al. 2024

Applied Sciences

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The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

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A comparative study of different algorithms using contrived failure data to detect robot anomalies

Cited by 8 publications

References 16 publications

Anomaly Detection Methods in Autonomous Robotic Missions

Anomaly Detection Methods in Autonomous Robotic Missions

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

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