Independent component analysis model utilizing de-mixing information for improved non-Gaussian process monitoring

• We focus on the de-mixing matrix, which is rarely studied in ICA model, to extract data information for fault detection.
• Multi-block strategy is employed to deal with big data in a novel way.
• The numerous data is divided through a similarity index Generalized Dice’s coefficient.
• Bayesian inference is also employed to combine the results with noise weakened.
• The way of fault diagnosis is modified with selected variables checked.

The de-mixing matrix generated from independent component analysis (ICA) can reveal information about the relations between variables and independent components, but the traditional ICA model does not preserve the whole de-mixing information for the purpose of feature extraction and dimensionality reduction, so that some important information may be abandoned. Multi-block strategy has been improved to be an efficient method to deal with numerous data. However, the manner of dividing original data is still subject for discussion and the priori knowledge is necessary for process division. This paper proposes a totally data-driven ICA model that divides de-mixing matrix based on the Generalized Dice’s coefficient and combines the results from sub-blocks using Bayesian inference. All information in de-mixing matrix is fully utilized and the ability of monitoring non-Gaussian process is improved. Meanwhile, a corresponding contribution plot is developed for fault diagnosis to find the root causes. The performance of the proposed method is illustrated through a numerical example and the Tennessee Eastman benchmark case study.