Haiyang Hao

TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.

TL;DR: A subspace-aided data-driven approach for batch process monitoring that serves as a non-parametric way of estimating the probability density function and an industrial benchmark of fed-batch penicillin production is used to verify the effectiveness of the proposed approach.

TL;DR: The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

TL;DR: The key performance indicator-based multivariate statistical process monitoring and fault diagnosis methods for linear static processes are surveyed and evaluated using the multivariate statistics framework and are broadly classified into three categories: direct, linear regression-based, and PLS-based.

TL;DR: The data-driven design of diagnostic-observer-based process monitoring schemes is extended to include the ability to detect changes given infrequently measured KPIs, and the extended diagnostic observer is shown to be stable and hence able to converge to the true value.