Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System

Luo, Ren C.; Wang, Hao

doi:10.1109/vtcfall.2018.8690710

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…This kind of task mainly uses binary classification or multi-category classification algorithms to predict failures or malfunctions. Luo and Wang [ 24 ] applied random forest to identify the malfunction of robot arms by learning patterns from the torque sensors. However, there is a lack of models to predict the remaining lifetime of a machine because there are not enough indicators to measure the health status of a machine [ 25 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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“…odern industries are moving toward informatization and intelligentization in the fourth industrial revolution era [1], and modern machinery and equipment are widely used in various fields, such as construction, aviation, electric power, and metallurgy. Given the inevitable faults of mechanical devices, health management has been studied for economic benefit and personnel security [2].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions

Chen

Shen

et al. 2022

IEEE Trans. Ind. Inf.

125

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Recently, various fault diagnosis methods based on domain adaptation (DA) have been explored to solve the problem of discrepancy between the source and target domains. However, given complex industrial scenarios, DA-based methods usually fail when the working conditions of machines are unseen, i.e., target data are unavailable during model training. In this work, a generic domain-regressive framework for fault diagnosis, namely, adversarial domain-invariant generalization (ADIG), is proposed. ADIG leverages multiple available domain data to exploit domain-invariant knowledge through adversarial learning between the feature extractor and the domain classifier. Simultaneously, the fault classifier generalizes the knowledge from the source-related domain to diagnose the unseen but related target domain signals. Moreover, customized strategies of feature normalization and adaptive weight are proposed to promote diagnosis performance. Comprehensive case studies show that ADIG achieves satisfactory diagnosis accuracy and robustness under unseen conditions, indicating that ADIG is a remarkably potential diagnosis tool for real-case industrial machines.

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AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

Mueller

Mezhuyev

2022

Studies in Computational Intelligence

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Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System

Cited by 10 publications

References 16 publications

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions

AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

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