Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph

Yan, Wei; Shi, Yu; Ji, Zengyan; Sui, Yuan; Tian, Zhenzhen; Wang, Wanjing; Cao, Qiushi

doi:10.1016/j.engappai.2023.106798

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…Lean 4.0 application For Alenany et al (2021), the hydraulic systems have great importance to industrial production activities, forming the core of most industrial units. In this context, predictive maintenance performs with condition monitoring to avoid premature failures, which makes it a crucial strategy for these types of systems (Yan et al, 2023).…”

Section: Reability Improvement Performance Mesuresmentioning

confidence: 99%

“…There are some studies on the application of Lean concepts in steel plants, like Raj et al (2021), where they present research that aims to improve the overall effectiveness of a steel plant through the concepts of Kaizen, and TPM, which enables to visualize and understand the losses elimination. Other studies like Yan et al (2023) reveal that hydraulic systems are growing to more intelligent and automated approaches that leverage Industry 4.0, allowing monitoring and prediction of the possibility of equipment failure and supporting appropriate maintenance strategies. The research of Rahardjo et al (2023) presented an industrial case study concerning the implementation of Lean 4.0 tools, which perform synergistic relationships to optimize production processes and provide management insights.…”

Section: Steel Industrymentioning

confidence: 99%

“…(2021), where they present research that aims to improve the overall effectiveness of a steel plant through the concepts of Kaizen, and TPM, which enables to visualize and understand the losses elimination. Other studies like Yan et al. (2023) reveal that hydraulic systems are growing to more intelligent and automated approaches that leverage Industry 4.0, allowing monitoring and prediction of the possibility of equipment failure and supporting appropriate maintenance strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

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Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Torre,

Bonamigo

2024

JQME

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PurposeMaintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.Design/methodology/approachThe maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.FindingsThe main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.Originality/valueThis research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

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Section: Reability Improvement Performance Mesuresmentioning

confidence: 99%

Section: Steel Industrymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Torre,

Bonamigo

2024

JQME

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“…Initially developed for extracting knowledge from extensive datasets, knowledge graphs are now a cornerstone in the semantic web, setting a benchmark for efficient information retrieval and usage. Their utility spans various sectors, notably in industry for tasks such as maintenance planning of sophisticated equipment (Xia et al, 2023), and predictive maintenance for hydraulic systems (Yan et al, 2023). Within the maritime and shipping sector, knowledge graphs have found applications in analyzing ship collision accident reports to enhance maritime traffic safety.…”

Section: Rationalementioning

confidence: 99%

Knowledge Graphs underpinning ship digital twins for decarbonisation options assessment

Karakostas,

Antonopoulos

2024

IMDC

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We propose the concept of a Knowledge Graph as a data management and inference machinery that underpins digital twins of ships. The Knowledge Graph is a directed graph connecting dependent and independent model variables of interest in the digital twin, where the correlations between variables are continuously updated based on data received from the physical ship. The paper outlines a methodology for constructing the Knowledge Graph and proposes metrics that help to calculate the effectiveness of decarbonization solutions based on changes to the strength of data correlations. The proposed methodology allows for the extrapolation of decarbonization technology potential across specific vessels, fleets, operational patterns, and lifecycle phases.

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“…Researchers have proposed a number of ontologies and models for integrating machine-related data and for predictive maintenance [25][26][27]. Ontology and knowledge graph-based approaches have also been proposed to handle semantic interoperability issues in maintenance [23,28]. However, when we narrow down the solutions, they often address specific tasks or scenarios, for example, pilot-specific ones.…”

Section: Alignment With Existing Studiesmentioning

confidence: 99%

Interoperable Information Flow as Enabler for Efficient Predictive Maintenance

Franke,

Deng,

Kyroudis

et al. 2024

Analytics

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Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.

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Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph

Cited by 13 publications

References 36 publications

Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Knowledge Graphs underpinning ship digital twins for decarbonisation options assessment

Interoperable Information Flow as Enabler for Efficient Predictive Maintenance

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