Random forest regression for predicting an anomalous condition on a UR10 cobot end-effector from purposeful failure data

Wescoat, Ethan; Krugh, Matthew; Mears, Laine

doi:10.1016/j.promfg.2021.06.064

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…Firstly, it is worth remarking that, in each joint of a cobot, some variables may be correlated, especially the temperature and the load (Aliev & Antonelli, 2021). Afterwards, Wescoat et al, (2021) point out how the starting position of the cobot may influence the model performance. Overall, all the presented solutions adopt a oneshot approach, that is, trying to predict the health state of the collaborative robots through one algorithm only, fitted to the specific case under study.…”

Section: Content Analysis and Resultsmentioning

confidence: 99%

“…As pointed out by the work of Wescoat et al, (2021), the precise identification of the starting point guarantees higher performance of models. As such, considering the industrial practice, it is possible to assume that there is a unique rest pose for all the possible trajectories that allows for an easy characterisation of the data series, i.e., the identification of all cycles.…”

Section: Experimental Campaignmentioning

confidence: 99%

“…Each algorithm has different performance with respect to the training, validation and test accuracy and a mixture of algorithms is suggested. The cobot under study was a Universal Robots UR10 and the work is an extension of the one below reported, namely (Wescoat et al, 2021). (Aliev & Antonelli, 2021) This research stems from the safety legislations needs for cobots to use real time and online data to predict failures and safe stops.…”

Section: Concluding Remarks From the Literature Reviewmentioning

confidence: 99%

“…The cobot under study was a Universal Robots UR3. (Wescoat et al, 2021) The objective is to describe the health state of a collaborative robot by means of a Random Forest Regression with varying predictors. Failure data are simulated by adding weights (from 0.2 Kg to 1.0 Kg) to the end-effector.…”

Section: Concluding Remarks From the Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Content Analysis and Resultsmentioning

confidence: 99%

Section: Experimental Campaignmentioning

confidence: 99%

Section: Concluding Remarks From the Literature Reviewmentioning

confidence: 99%

Section: Concluding Remarks From the Literature Reviewmentioning

confidence: 99%

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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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“…Another used machine learning algorithm in the hydrate community is random forest (RF). The RF algorithm predictions result from each tree in the decision trees . Its advantages are based on its simplicity of implementation in parallel, fast training, and prediction.…”

Section: Machine Learning Models Used For Gas-hydrate-related Studiesmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement

Meddaoui,

Hachmoud,

Hain

2024

Int J Adv Manuf Technol

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In order to achieve an optimal system performance, decision makers are continually faced with the responsibility of making choices that will enhance availability and reduce failures cost. To realize this goal, it is crucial to ensure the timely maintenance of equipment, which often poses a signi cant challenge. However, the adoption of Predictive Maintenance (PdM) technology can offer a solution by enabling real-time maintenance, resulting in various bene ts such as reduced downtime, cost savings, and enhanced production quality. Machine learning (ML) techniques are increasingly being used in the eld of predictive maintenance to predict failures and calculate estimated remaining useful life (RUL) of equipment. A case study is proposed in this research paper based on a maintenance dataset from the aerospace industry. It experiments and compare multiple combination of feature engineering techniques and advanced ML models with the aim to propose the most e cient techniques for prediction. Moreover, future research papers can focus on the challenge of validating this proposed model in different industrial environments.

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Random forest regression for predicting an anomalous condition on a UR10 cobot end-effector from purposeful failure data

Cited by 7 publications

References 25 publications

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

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

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement

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