Reviewing the Challenges in Maintaining the Reliability and Accuracy of IOT Systems for Remaining Useful Life Prediction
Open accessAbstract
Internet of things (IoT) has been implemented in aviation predictive maintenance in recent years for the enhancement of better maintenance prediction, to reduce downtime, unnecessary maintenance actions, increase safety, increase system readiness, and refine the management process and to improve component design. The IoT system in predictive maintenance is very optimistic in gathering and analysing, predicting the component failures and to determine the remaining useful life of a systems. Since Remaining useful life of an system is defines as the length from the current time to the end of its useful life. Due to its futuristic increasing demand of IoT in aviation maintenance, the biggest challenge is to ensuring the reliability and accuracy of any specific IoT system allotted for monitoring aircraft components in the near future. Hence, this review paper clearly explains the challenges associated with IoT systems on predicting Remaining useful life.
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Siemens introduced MindSphere in 2016 as a commercial IIoT service, forming the core of its IIoT strategy.
“Siemens is a remarkable example of a business embracing IIoT, with its platform brand, MindSphere, introduced in 2016 as a commercial IIoT service catering not only to manufacturing customers but also extending its reach beyond, forming the core of the company's IIoT strategy [29].”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [29]
The Introduction discusses IoT reliability and remaining-useful-life prediction, while the retrieved References passages list related IoT studies (pp. 11–12). Searches for MindSphere, Siemens, its 2016 introduction, and its IIoT strategy found no passage in our copy addressing the claim. (LLM, conf=0.76)
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Sorted by citations
Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38 · see citing works
“1)The heterogeneity of IoT devices presents reliability challenges with limitations in hardware and software components, affecting parameters like throughput and data accuracy [29], [30].”
BackgroundSatisfaction with the Implementation of Industry 4.0 Among Manufacturing Companies in Poland
Żywiołek, J., Molenda, M., & Rosak-Szyrocka, J. (2021) · EUROPEAN RESEARCH STUDIES JOURNAL · doi:10.35808/ersj/2484 · Cited by 2 · see citing works
Wankhede, V. A., & Vinodh, S. (2022) · Lecture notes in mechanical engineering · doi:10.1007/978-981-19-0561-2_11
- Authors
- Sekar, K., Shah, S. A., & Antony Athithan, A.
- Affiliations
- Lincoln University College (MY)
- University of Kuala Lumpur (MY)
- Corresponding author
- Shah, Shahani Aman
- Venue
- IOP Conference Series: Materials Science and Engineering
- Publisher
- IOP Publishing
- Published
- 2021-04-01
- Volume
- 1128
- Issue
- 1
- Pages
- 012010
- ISSN
- 1757-8981 (ISSN-L)
- 1757-899X
- OpenAlex
- W3157907792
- Cited by
- 3
- References
- 43
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 1.84
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- IOP Conference Series Materials Science and Engineering (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Keywords
- Internet of Things, remaining useful life prediction, system reliability, prediction accuracy, component failure prediction
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