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Abstract: In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis–principal component analysis (FKICA–PCA), is developed. In FKICA–PCA, first, a method to calculate the variance of independent component is proposed, which is significant to make Gaussian features and non-Gaussian features comparable and to select dominant components legitimately; second, Genetic Algorithm is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate; furthermore, exponentially weighted moving average (EWMA) scheme is used to filter the monitoring indices of KICA–PCA to improve monitoring performance. In addition, a novel contribution analysis scheme is developed for FKICA–PCA to diagnosis faults. The feasibility and effectiveness of the proposed method are validated on the Tennessee Eastman (TE) process. [Copyright &y& Elsevier]

Abstract: In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector AR model with wavelet based denoising. Given the fault direction, a new index is proposed to detect the fault, which integrates fault detection and prognosis together. Case studies on a continuous stirred tank reactor and the Tennessee Eastman process demonstrate the effectiveness of the proposed approaches. [Copyright &y& Elsevier]

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities. [Copyright &y& Elsevier]

Abstract: The continuous annealing process line (CAPL) has complex process characteristics, such as strong correlation of a large number of process variables and interconnected multi-subsystems and multiple operation zones. Practitioners are concerned with typical process faults, such as strip-break and roll-slippage, whose effects are often confined in a specific zone. Considering the large-scale process characteristics and fault characteristics, a multi-block fault diagnosis method is proposed. A novel reconstruction-based block contribution (RBBC) is first proposed in order to diagnose the faulty block. The reconstruction-based variable contribution (RBVC) within a block is also proposed to determine the faulty variables. The proposed RBBC–RBVC hierarchical scheme is applied successfully to a real CAPL on two fault cases. A finite state machine is utilized to diagnose strip-break and reconstructed combined index is studied to diagnose roll-slippage. [Copyright &y& Elsevier]

Fault diagnosis is essential for troubleshooting and maintenance of industrial processes that operate dynamically. Traditional reconstruction-based fault diagnosis methods, however, are mostly developed for static processes and are ineffective for faults with similar directions. In this paper, a new hierarchical fault diagnosis strategy that incorporates reconstruction and dynamic time warping is proposed for the feeding anomaly diagnosis of an industrial cone crusher. A novel fault-magnitude-estimation method for dynamic processes is proposed based on the dynamic relations captured by dynamic latent variable (DLV) predictions. A combined index is developed based on the prediction residuals which exclude normal and predictable variations to improve sensitivity to faults. Fault-magnitude-estimation-based dynamic time warping is proposed to evaluate the shape similarity of faults in order to further isolate the fault candidates with similar directions. The reconstructed magnitude is utilized to extract shape features of the faults. The advantages are demonstrated using a Monte-Carlo simulation example of a dynamic process. Finally, the proposed method is applied successfully to diagnose feeding anomalies of an industrial cone crusher. [ABSTRACT FROM AUTHOR]