Development of Predictive Maintenance Technology for Wafer Transfer Robot using Clustering Algorithm

Kim, Hyeong-Gyun; Yoon, Hee-Seung; Yoo, Jaewon; Yoon, Hyun-il; Han, Seung-Soo

doi:10.23919/elinfocom.2019.8706485

Cited by 9 publications

(10 citation statements)

References 3 publications

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“…The vibration and 1D-CNN-based works are common for the CM and fault assessment of machine components [24,25], structural systems [26], and industrial robots [27]. Other CM and fault diagnosis works for industrial robots include minimising downtime in a wafer transfer robot [28], failure prediction in a packaging robot [29], and a safe stop for a collaborative robot [30]. However, such CM works are not extended for outdoor mobile robots, though a great research scope using the typical onboard sensors and a necessity because of their vast market demand and safety.…”

Section: Related Workmentioning

confidence: 99%

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

Pookkuttath,

Abdulkader,

Elara

et al. 2023

Mathematics

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An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.

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Section: Related Workmentioning

confidence: 99%

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

Pookkuttath,

Abdulkader,

Elara

et al. 2023

Mathematics

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“…The authors of [64] provided an efficient solution to the problem of irregular, unbalanced, and unlabeled data, where conventional methods were outdated. A balanced random forest model was developed for unbiased classification and regression.…”

Section: Decision Treementioning

confidence: 99%

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Gong

Chen

et al. 2022

Micromachines

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The necessity of vehicle fault detection and diagnosis (VFDD) is one of the main goals and demands of the Internet of Vehicles (IoV) in autonomous applications. This paper integrates various machine learning algorithms, which are applied to the failure prediction and warning of various types of vehicles, such as the vehicle transmission system, abnormal engine operation, and tire condition prediction. This paper first discusses the three main AI algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, and compares the advantages and disadvantages of each algorithm in the application of system prediction. In the second part, we summarize which artificial intelligence algorithm architectures are suitable for each system failure condition. According to the fault status of different vehicles, it is necessary to carry out the evaluation of the digital filtering process. At the same time, it is necessary to preconstruct its model analysis and adjust the parameter attributes, types, and number of samples of various vehicle prediction models according to the analysis results, followed by optimization to obtain various vehicle models. Finally, through a cross-comparison and sorting, the artificial intelligence failure prediction models can be obtained, which can correspond to the failure status of a certain car model and a certain system, thereby realizing a most appropriate AI model for a specific application.

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“…Fuzzy k-means and adaptive neuro-fuzzy inference have also been applied to predict a distillation column's remaining useful life in the chemical industry (Daher et al, 2020). K-means clustering on its own has also been used for the predictive maintenance of a wafer transfer robot (Kim et al, 2019). Amruthnath & Gupta (2018) proposed a comparison among different clustering approaches, namely hierarchical clustering, k-means, and fuzzy k-means, to compare their performance in terms of fault prediction and select the best model.…”

Section: Clustering Of Data Using Fuzzy K-meansmentioning

confidence: 99%

Entropy-MAUT integrated approach supported by Fuzzy K-means: a robust tool for determining critical components for maintenance monitoring and a case study of Kaplan hydro generator unit

Bellinello,

Antomarioni,

Souza

et al. 2022

Prod.

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Paper aims: This paper aims to develop a proper maintenance policy directly related to defining critical components for ensuring a high level of safety and high-level in-service quality for all hydro generator units.Originality: An innovative integrated tool that contributes to ensuing assertiveness in decision-making to determine the critical components is presented in this study. Specifically, hydro-generator unit type Kaplan belonging to a Brazilian Hydroelectric power plant is used as an application case to highlight the choice of the most suitable maintenance policy in light of the proposed approach. The selection of the case study is based on the fact that hydroelectric power plants are the basis of the Brazilian energy matrix, accounting for 75% of the demand in the country. Therefore, the need to maintain hydroelectric plants' availability and operational reliability is clear not to compromise the continuity and conformity (quality) of the electrical energy supply.Research method: Seven multi-criteria decision-making methods were applied in addition to two methods for deciding weight (Critic Method and Entropy) have been compared to determine the critical components of the hydro-generator. To investigate the robustness of the classification of the applied Multi-Criteria Decision Making approaches, a sensitivity analysis was performed based on the weight change of each decision criterion. Main findings:As a main result, the Entropy-Multi-Attribute Utility Theory model is proposed as the best approach to guarantee the selection of critical components for the Brazilian hydroelectric power plant case study. The validation sensitivity analysis by critical Fuzzy K-means groups guarantees that it is a robust tool for decision-making.Implications for theory and practice: Ensuring the availability and reliability of hydroelectric plants can be achieved by employing appropriate maintenance policies that reduce the likelihood of failure or even eliminate its root causes, preventing failure from occurring. Consequently, a robust tool for decision-making regarding the Kaplan hydro generator's critical components' monitoring was developed.

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Development of Predictive Maintenance Technology for Wafer Transfer Robot using Clustering Algorithm

Cited by 9 publications

References 3 publications

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Entropy-MAUT integrated approach supported by Fuzzy K-means: a robust tool for determining critical components for maintenance monitoring and a case study of Kaplan hydro generator unit

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