Data-Driven Residual-Based Fault Detection for Condition Monitoring in Rolling Mills

Serdio, Francisco; Lughofer, Edwin; Pichler, Kurt; Buchegger, Thomas; Efendic, Hajrudin

doi:10.3182/20130619-3-ru-3018.00403

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Literature Review On Evolving Fuzzy Systems1

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2023

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Cited by 4 publications

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“…Lughofer and Kindermann [79] presented the sparse fuzzy inference systems (SparseFIS), a model that optimizes the consequent parameters and sparses out unimportant rules. SparseFis uses a numerical optimization mechanism to define a compact ruleset [80]. Leite et al [8] exploited the fuzzy set based evolving modeling (FBeM), a framework that employs fuzzy granular models to provide a more intelligible exhibition of the data.…”

Section: Literature Review On Evolving Fuzzy Systemsmentioning

confidence: 99%

A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers

Alves

Aguiar

2021

Applied Soft Computing

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Section: Literature Review On Evolving Fuzzy Systemsmentioning

confidence: 99%

A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers

Alves

Aguiar

2021

Applied Soft Computing

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Prediction and analysis of cold rolling mill vibration based on a data-driven method

Lü

Sun

Song

et al. 2020

Applied Soft Computing

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Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

Alzghoul

2023

Advances in Operations Research

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Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

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Data-Driven Residual-Based Fault Detection for Condition Monitoring in Rolling Mills

Cited by 4 publications

References 11 publications

A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers

A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers

Prediction and analysis of cold rolling mill vibration based on a data-driven method

Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

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