An Approach Based on Improved Grey Model for Predicting Maintenance Time of IPS2

Liu, Yuji; Hu, Yaoguang; Zhou, Rui; Wen, Jinyu

doi:10.1016/j.procir.2016.03.047

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Chiu et al [15] perform predictive maintenance in the factory based on predictive maintenance systems and propose health indicator hierarchy. Liu et al [16] proposed that predictive maintenance use lots of big data technology. Using the predictive maintenance method of the recurrent neural network, it is possible to find out the data relationships in a specific time series.…”

Section: Fdc Fault Detection and Classificationmentioning

confidence: 99%

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Apply Machine Learning to Improve Fault Detection and Classification in Cyber Physical System

Tsai¹,

Chiu²

2019

Advances in Transdisciplinary Engineering

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Cyber Physical System (CPS) is one of the key technologies of industry 4.0. CPS is an integrated system that combines computing, sensors and actuators. CPS is controlled by computer-based algorithms that integrates people and internet. However, the performance of CPS is limited by its computational complexity. How to implement CPS with less computational complexity in a real time performance remains an important issue. Therefore, the study attempts to establish a CPS based on machine learning to reduce its complexity and improve applicability. This study applies a Fault Detection and Classification system (FDC) with machine learning method in the virtual subsystem of CPS. This study consists of two steps. Based on the data collected from sensors of CPS, Random Forest (RF) is used to figure out key factors that reduces the data dimension for less computational complexity. The process engineers will know which factors are causing the machine fails. The second step is to establish a time series model which bases on Long Short-Term Memory (LSTM) network to achieve real time monitoring of the machine. Before the machine fails, FDC will alarm that allow the engineer to adjust parameters of machines to avoid machine fails. The study in order to achieve the goals of predicting machines fails, figuring out key factors, predicting maintenance and increasing the productivity.

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Section: Fdc Fault Detection and Classificationmentioning

confidence: 99%

“…In addition, "12. Wire guide roll bearing temp mov 1", " 16. Wire guide roll bearing temp mov 2", and "8.…”

Section: Case Studymentioning

confidence: 99%

Apply Machine Learning to Improve Fault Detection and Classification in Cyber Physical System

Tsai¹,

Chiu²

2019

Advances in Transdisciplinary Engineering

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“…This method can be successfully applied with or without other pavement maintenance treatments [13]. Yuji et al proposed an improved grey prediction model that obtained the prediction interval of maintenance service time and used it as the basis for active maintenance service decision-making [14]. Currently, numerous research studies exist on the costeffectiveness analyses of pavement maintenance using materials.…”

Section: Introductionmentioning

confidence: 99%

Integrating grey relation sorting and cost-benefit analysis for optimal asphalt pavement maintenance prioritisation

2024

JCE

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making problem. The common approach involves the prioritisation of road repairs with more extensive surface damages but this does not always align with the actual repair needs. To address this, a method based on the grey correlation theory was introduced to evaluate pavement performance. This method was employed to determine the optimal maintenance time for asphalt pavements. Once the sequence was established, the ideal maintenance schedule for the pre-maintenance sections was determined using a cost–benefit model. This evaluation method is particularly useful for quantitative analyses of the influences of pavement age and traffic volume on pavement performance parameters. The results demonstrate that grey relation sorting effectively weighs the factors and provides rankings for road segments requiring maintenance. A cost–benefit analysis model was used to assess the maintenance sections. This model can guide decisions regarding pre-maintenance sections and assess pavement conditions, thus resulting in outcomes that align better with real-world situations and human judgment. These findings are expected to be valuable for the long-term maintenance of asphalt pavements.

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Digital technologies in product-service systems: a literature review and a research agenda

Pirola

Boucher

Wiesner

et al. 2020

Computers in Industry

157

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An Approach Based on Improved Grey Model for Predicting Maintenance Time of IPS2

Cited by 6 publications

References 4 publications

Apply Machine Learning to Improve Fault Detection and Classification in Cyber Physical System

Apply Machine Learning to Improve Fault Detection and Classification in Cyber Physical System

Integrating grey relation sorting and cost-benefit analysis for optimal asphalt pavement maintenance prioritisation

Digital technologies in product-service systems: a literature review and a research agenda

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