Vertices Packaging-Based Interval Independent Component Analysis (VP-I2CA) for Fault Detection With Process Uncertainty

The fault detection capability of traditional data-driven process monitoring methods is highly dependent on the quality of process data. However, affected by measurement noise, harsh operation scenarios and other factors, the process data are inevitably contaminated by uncertainty in real processes. In this article, a vertices packaging-based interval independent component analysis (VP-I²CA) method is proposed to monitor the uncertain non-Gaussian processes. First, a variable bandwidth-kernel density estimation-based measurement error estimation method is developed to describe the uncertainty-contaminated process data in interval form using limited reliable data samples. Then, VP-I²CA is developed to estimate the demixing matrix based on hypermatrices constructed by vertices encoding, which includes all possible combinations between the bounds of interval data by explicitly considering the existence of uncertainty. In order to reduce computational complexity, the idea of data packaging is introduced to represent the hyper-independent components in interval form with a series of values by packaging all possible feature information hidden in uncertain process data. Afterwards, four monitoring statistics are constructed to monitor the systematic and nonsystematic parts of process operation variation. The proposed algorithm is verified in both a six-variable numerical simulation system and a continuous stirred tank reactor system.