Distribution Dynamic Direct Orthogonal Decomposition Method for Quality-Related Fault Detection

Traditional centralized modeling and fault detection methods for large-scale industrial processes have limitations, including a significant computational load and reduced performance. To address these issues, this paper proposes a distributed dynamic direct orthogonal decomposition method for quality-related fault detection in large-scale industrial processes. This method first decomposes the industrial process to several subunits based on its inherent mechanism. To fully consider the coupling relationship between subunits and improve the communication efficiency among them, the representative variables within each subunit are first selected based on the cosine function. On this basis, regression equations are established between the representative variables of each local subunit and those of its adjacent subunits using LASSO. Then, relevant adjacent unit variables are selected based on the regression coefficients to achieve effective information exchange between the local and adjacent subunits. For the reconstructed local subunits, a dynamic direct orthogonal decomposition method is proposed to achieve quality-related fault detection. In the proposed fault detection method at the subunit level, to better capture the dynamics within the data, the time-delay factor is first introduced to the process variables and the quality variables, and the load matrix of the process variables and the quality variables is obtained using standard partial least squares. Subsequently, the covariance matrix of the load matrix is decomposed based on singular value decomposition to construct an orthogonal decomposition matrix, thereby achieving orthogonal division of the process variables based on the quality variables within each subunit. To derive a more concise detection logic, the Bayesian fusion strategy is adopted to integrate the statistical indicators corresponding to the same type of faults detected in each subunit. Finally, the effectiveness of this method is verified through an industrial example.