Department of Computer Science, Zhejiang Normal University, Jinhua 321004, China.
Xingzhi College, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel). 2018 Oct 6;18(10):3338. doi: 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.
过程参数估计在很大程度上决定了工业生产质量。然而，传统的有线方式只能部署有限的传感器，这导致在恶劣环境中过程参数估计效果不佳。工业无线传感器网络（IWSN）是一种通过灵活的传感器部署来丰富采样点，并通过协作信号去噪来净化目标的技术。本文关注的是过程工业场景，其中工件在皮带上传输，在下一个过程阶段之前需要进行参数估计。为此，提出了一种基于一致性的顺序估计（CSE）框架，该框架利用 IWSN 和参数状态估计的协同设计。首先，提供了一种基于群组的网络部署策略和基于时分多址（TDMA）的调度方案，以跟踪和采样移动的工件。然后，通过与定制的 IWSN 匹配，开发了基于一致性卡尔曼估计的顺序估计算法，并在不确定的无线通信条件下推导了最小均方误差（MSE）的最优估计器。最后，提供了一个在热铣削过程中温度估计的案例研究。结果表明，尽管在现有系统中使用单点温度传感器的测量误差可能超过 100 ∘ C，但在有限的时间内，估计误差可以降低到 3 ∘ C 以下。
