School of Management, Hefei University of Technology, Hefei 230009, China.
Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China.
Sensors (Basel). 2019 Sep 22;19(19):4099. doi: 10.3390/s19194099.
In industrial production, soft sensors play very important roles in ensuring product quality and production safety. Traditionally, global modeling methods, which use historical data to construct models offline, are often used to develop soft sensors. However, because of various complex and unknown changes in industrial production processes, the performance of global models deteriorates over time, and frequent model maintenance is difficult. In this study, locally weighted partial least squares (LWPLS) is adopted as a just-in-time learning method for industrial soft sensor modeling. In LWPLS, the bandwidth parameter h has an important impact on the performance of the algorithm, since it decides the range of the neighborhood and affects how the weight changes. Therefore, we propose a two-phase bandwidth optimization strategy that combines particle swarm optimization (PSO) and LWPLS. A numerical simulation example and an industrial application case were studied to estimate the performance of the proposed PSO-LWPLS method. The results show that, compared to the traditional global methods and the LWPLS with a fixed bandwidth, the proposed PSO-LWPLS can achieve a better prediction performance. The results also prove that the proposed method has apparent advantages over other methods in the case of data density changes.
在工业生产中，软测量技术在保证产品质量和生产安全方面发挥着非常重要的作用。传统上，常采用全局建模方法，即利用历史数据离线构建模型，来开发软测量技术。然而，由于工业生产过程中存在各种复杂和未知的变化，全局模型的性能会随着时间的推移而下降，模型的频繁维护变得困难。在本研究中，局部加权偏最小二乘法（LWPLS）被用作工业软测量建模的即时学习方法。在 LWPLS 中，带宽参数 h 对算法的性能有重要影响，因为它决定了邻域的范围，并影响权重的变化方式。因此，我们提出了一种两阶段带宽优化策略，将粒子群优化（PSO）和 LWPLS 相结合。通过数值模拟示例和工业应用案例研究了所提出的 PSO-LWPLS 方法的性能。结果表明，与传统的全局方法和具有固定带宽的 LWPLS 相比，所提出的 PSO-LWPLS 可以实现更好的预测性能。结果还证明，在所研究的案例中，与其他方法相比，所提出的方法在数据密度变化时具有明显的优势。
