Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance
By Jihong Yan, Yue Meng, Lei Lu, Lin Li. IEEE Access, 2017
357 citations recorded by the source corpus.
Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.
Subfields: Industrial and Manufacturing Engineering, Medical Laboratory Technology
View Paper: https://www.semanticscholar.org/paper/b21aaef087ec31e7c869278f123d222720dab8d9
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In Industrial and Manufacturing Engineering
Citations in the corpus
- Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, background): “Another work investigated the remaining lifetime detection of key machinery components for predictive maintenance purposes by analyzing heterogeneous data that were collected through multiple sources [64].”
- A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard (2018, background): “…sensors, networking, and robotics, together with paradigms like IoT, enable the development of advanced applications for industrial systems [25], energy efficiency [26], [27], home automation [28], precision agriculture [29], high-security IoT applications [30]–[34], transportation [35] or…”
- Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, background): “Afterwards, through the assessment of maintenance effectiveness of different maintenance policies, relevant costs, resources and the particular context at hand, the optimal maintenance strategy can be determined [73].”
- Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges (2018, methodology): “Energy saving for production management is performed through intelligent optimization algorithms to optimize total energy consumption with a big data processing platform [71].”
- The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review (2020, background): “Yan et al. (2017) propose a framework for structuralising multisource heterogeneous industrial big data, and mining the regulatory energy-saving mechanism.”
- Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “It has become a necessity in the industrial scenario as undetected failures can lead to a critical damage [4].”