In this paper, a new data‐driven sensor fault detection and isolation (FDI) technique for interval‐valued data is developed. The developed approach merges the benefits of generalized likelihood ratio (GLR) with interval‐valued data and principal component analysis (PCA). This paper has three main contributions. The first contribution is to develop a criterion based on the variance of interval‐valued reconstruction error to select the number of principal components to be kept in the PCA model. Secondly, interval‐valued residuals are generated, and a new fault detection chart‐based GLR is developed. Lastly, an enhanced interval reconstruction approach for fault isolation is developed. The proposed strategy is applied for distillation column process monitoring and air quality monitoring network.

中文翻译：

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基于新传感器故障检测和隔离策略的区间值数据

在本文中，开发了一种新的数据驱动传感器故障检测和隔离 (FDI) 技术，用于间隔值数据。所开发的方法将广义似然比 (GLR) 与区间值数据和主成分分析 (PCA) 的优点相结合。本文有三个主要贡献。第一个贡献是基于区间值重构误差的方差开发了一个标准，以选择要保留在 PCA 模型中的主成分的数量。其次，生成区间值残差，并开发了一种新的基于故障检测图的 GLR。最后，开发了一种用于故障隔离的增强间隔重建方法。所提出的策略应用于蒸馏塔过程监控和空气质量监控网络。