A Global and Local Batch Process Fault Detection Method Based on Dynamic Orthogonality

Abstract

The present invention provides a global and local intermittent process fault detection method based on dynamic orthogonality. I×J×K ; (2) First expand the training sample X along the batch direction into two-dimensional data X∈R I×KJ , and then normalize the expanded two-dimensional data, and rearrange the normalized two-dimensional data into X∈R KI×J ; (3) In two-dimensional data X∈R KI×J Based on that, build a delay matrix X D to eliminate the time series autocorrelation of process variables; (4) for the established time-lag matrix X D Construct a dynamic orthogonal global local model; (5) establish T in the feature space and residual space respectively 2 and SPE statistical model to obtain control limits; (6) collect online process data and perform standardized processing; (7) use the established dynamic orthogonal global local model to project online data, and pass T 2 and SPE statistical model to judge the occurrence of faults.

Description

technical field [0001] The invention belongs to the technical field of industrial process monitoring and relates to a dynamic orthogonal-based global and local intermittent process fault detection method. Background technique [0002] With the continuous expansion of the scale and complexity of modern industrial processes, batch process as an important production method is widely used in high-quality and small-capacity process production, such as special chemical industry, food production, pharmaceutical production and semiconductor production Wait. Batch process production is composed of many production links, each link is crucial to the final product quality and safe production, and the completion of the previous link directly affects the implementation of the next link. Batch process production is more prone to failure due to the nesting of multiple stages, increasing production scale and increasing complexity. If the failure cannot be detected and eliminated in time, t...

AI Technical Summary

Problems solved by technology

The traditional method only considers the global structure of the process data and ignores the local structure of the data during feature extraction, so the global and local structure of the process data cannot be fully maintained in the process of dimensionality reduction, and it is not considered in the data reconstruction. The generalized orthogonal problem causes the loss of some useful information in the process monitoring, resulting in poor monitoring effect

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