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首页> 外文期刊>International journal of productivity and quality management >Process monitoring strategy for a steel making shop:a partial least squares regression-based approach
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Process monitoring strategy for a steel making shop:a partial least squares regression-based approach

机译：炼钢厂的过程监控策略：基于偏最小二乘回归的方法

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This paper deals with the process monitoring strategy for a Steel Making Shop (SMS). The process and the feedstock characteristics of the SMS were being simultaneously monitored for the detection of an upset condition or an out-of-control situation. Partial Least Squares Regression (PLSR), a multivariate projection-based technique was used for the development of the process representation. Henceforth, T~2 chart was used to monitor the process and the feedstock characteristics and the out-of-control observations were diagnosed with the aid of contribution plots. Contribution plots revealed the characteristic or the combination of the characteristics responsible for an out-of-control observation. Multivariate Hotelling's T~2 chart was also used for monitoring of the process and feedstock characteristics and the results thus obtained were compared with that of the PLSR-based T~2 chart. Data pertaining to the process and feedstock characteristics were collected for a period of six months. The PLSR-based T~2 chart was able to detect the out-of-control observations and the contribution plots aided in revealing the set of characteristics responsible for the out-of-control observations.
机译：本文讨论了炼钢车间（SMS）的过程监控策略。同时监测SMS的过程和原料特性，以检测异常情况或失控情况。偏最小二乘回归（PLSR）是一种基于多元投影的技术，用于开发过程表示。此后，使用T〜2图表监视过程，并借助贡献图对原料特性和失控观测值进行诊断。贡献图揭示了导致失控观察的特征或特征组合。多变量Hotelling的T〜2图还用于监控过程和原料特性，并将由此获得的结果与基于PLSR的T〜2图进行比较。在六个月的时间内收集了有关工艺和原料特性的数据。基于PLSR的T〜2图表能够检测出失控的观测值，而贡献图有助于揭示导致失控的观测值的特征集。

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