Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks Article Swipe
Feilong Lin
,
Wenbai Li
,
Liyong Yuan
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/s18103338
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/s18103338
Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next process stage. To this end, a consensus-based sequential estimation (CSE) framework is proposed which utilizes the co-design of IWSN and parameter state estimation. First, a group-based network deployment strategy, together with a TDMA (Time division multiple access)-based scheduling scheme is provided to track and sample the moving workpiece. Then, by matching to the tailored IWSN, the sequential estimation algorithm, which is based on the consensus-based Kalman estimation, is developed, and the optimal estimator that minimizes the mean-square error (MSE) is derived under the uncertain wireless communications. Finally, a case study on temperature estimation during the hot milling process is provided. The results show that the estimation error can be reduced to less than 3 ∘ C within a limited time period, although the measurement error can be more than 100 ∘ C in existing systems with a single-point temperature sensor.
Related Topics
Concepts
Wireless sensor network
Estimator
Real-time computing
Kalman filter
Process (computing)
Computer science
Time division multiple access
Wireless
Mean squared error
Process state
Scheduling (production processes)
Wireless network
Software deployment
Engineering
Artificial intelligence
Computer network
Telecommunications
Statistics
Mathematics
Operations management
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s18103338
- https://www.mdpi.com/1424-8220/18/10/3338/pdf?version=1539864372
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2896752794
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2896752794Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s18103338Digital Object Identifier
- Title
-
Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-06Full publication date if available
- Authors
-
Feilong Lin, Wenbai Li, Liyong YuanList of authors in order
- Landing page
-
https://doi.org/10.3390/s18103338Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/18/10/3338/pdf?version=1539864372Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/18/10/3338/pdf?version=1539864372Direct OA link when available
- Concepts
-
Wireless sensor network, Estimator, Real-time computing, Kalman filter, Process (computing), Computer science, Time division multiple access, Wireless, Mean squared error, Process state, Scheduling (production processes), Wireless network, Software deployment, Engineering, Artificial intelligence, Computer network, Telecommunications, Statistics, Mathematics, Operations management, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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