An integral approach to inferential quality control with self-validating soft-sensors

This paper presents an integral technique for designing an inferential quality control applicable to multivariate processes. The technique includes a self-validating soft-sensor and a multivariate quality control index that depends on the specifications. Based on a partial least squares (PLS) decomposition of the online process measurements, a fault detection and diagnosis technique is used to develop an improved self-validation strategy that is able to confirm, correct or reject the soft-sensor predictions. Model extrapolations, disturbances or sensor faults are first detected through a combined statistic (that considers the calibration region); then, a diagnosis is made by combining statistics pattern recognition, contribution analysis, and disturbance isolation based on historical fault patterns. An off-spec alarm is produced when the proposed index detects that an operating point lies outside the integral design space driven by the specifications. The effectiveness of the proposed technique is evaluated by means of two numerical examples. First, a synthetic example is used to interpret the fundamentals of the method. Then, the technique is applied to the industrial Styrene-Butadiene rubber process, which is emulated through an available numerical simulator.