A multigroup fault detection and diagnosis framework for large-scale industrial systems using nonlinear multivariate analysis

作者：

Highlights：

• A multigroup fault detection and diagnosis (FDD) framework for industrial systems.

• The gradKPCA and gradKCCA methods for the analysis of large-scale data sets.

• A method for dividing system variables into groups using the mutual information.

• Intra-group and inter-group FDD methods based on gradKPCA and gradKCCA.

摘要

•A multigroup fault detection and diagnosis (FDD) framework for industrial systems.•The gradKPCA and gradKCCA methods for the analysis of large-scale data sets.•A method for dividing system variables into groups using the mutual information.•Intra-group and inter-group FDD methods based on gradKPCA and gradKCCA.

论文关键词：Multigroup framework,Fault detection and diagnosis,Large-scale industrial system,Nonlinear multivariate analysis,Variable grouping

论文评审过程：Received 2 February 2022, Revised 9 June 2022, Accepted 10 June 2022, Available online 13 June 2022, Version of Record 22 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117859