Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks
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 technique...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3338 |
| _version_ | 1817989484638109696 |
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| author | Feilong Lin Wenbai Li Liyong Yuan |
| author_facet | Feilong Lin Wenbai Li Liyong Yuan |
| author_sort | Feilong Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:47:27Z |
| format | Article |
| id | doaj.art-df2c34e84dd2418f819496dd236695ae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:47:27Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-df2c34e84dd2418f819496dd236695ae2022-12-22T02:21:58ZengMDPI AGSensors1424-82202018-10-011810333810.3390/s18103338s18103338Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor NetworksFeilong Lin0Wenbai Li1Liyong Yuan2Department of Computer Science, Zhejiang Normal University, Jinhua 321004, ChinaDepartment of Computer Science, Zhejiang Normal University, Jinhua 321004, ChinaXingzhi College, Zhejiang Normal University, Jinhua 321004, ChinaProcess 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.http://www.mdpi.com/1424-8220/18/10/3338industrial wireless sensor networksconsensus-based sequential estimationco-design |
| spellingShingle | Feilong Lin Wenbai Li Liyong Yuan Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks Sensors industrial wireless sensor networks consensus-based sequential estimation co-design |
| title | Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks |
| title_full | Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks |
| title_fullStr | Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks |
| title_full_unstemmed | Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks |
| title_short | Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks |
| title_sort | consensus based sequential estimation of process parameters via industrial wireless sensor networks |
| topic | industrial wireless sensor networks consensus-based sequential estimation co-design |
| url | http://www.mdpi.com/1424-8220/18/10/3338 |
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