Evolution of multivariate statistical process control: application of independent component analysis and external analysis

Fault detection and diagnosis based on modified independent component analysis

“…The basic idea is to extract essential components that drive a process using ICA and to monitor the ICs instead of the original measurements. Also, Kano et al 16 proposed a new MSPM method based on ICA and external analysis to improve the monitoring performance and to distinguish faults from normal changes in operating conditions. However, in their method fault diagnosis is not considered, the number of monitoring charts increases as the number of ICs extracted from observed data, and false alarms may occur often given that the upper and lower control limits are devised from the assumption that the ICs follow Gaussian distributions.…”

Process monitoring with ICA‐based signal extraction technique and CART approach

“…Xia 29 and Xia and Howell 30 developed an ICA-based spectrum method to convert process data in the time axis into frequency space, and its offset type was then identified. Kano et al 31 successfully verified the effectiveness of process control regarding ICA converting post-process control. Lee et al 32,33 went a step further to discuss how to use kernel density estimation to define IC's control threshold values.…”

Importance of auxiliary systems for process fault detection and diagnosis

“…The methods with greatest impact are data-driven [7][8] and are mainly supported by dimensionality reduction techniques like Principal Component Analysis [9] and Independent Component Analysis [10]. Recent work [11][12] has successfully integrated data-driven analysis with process knowledge without quantitative models of the process, by using an electronic process schematic to extract the causal information in an automated way.…”