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Dynamic principal component analysis (DPCA) is widely used in the monitoring of dynamic multivariate processes. In traditional DPCA where a time window is used, the dynamic relations among process variables are implicit and difficult to interpret in terms of variables. To extract explicit latent variables that are dynamically correlated, a dynamic latent-variable model is proposed in this paper. The new structure can improve the modeling and the interpretation of dynamic processes and enhance the performance of monitoring. Fault detection strategies are developed, and contribution analysis is available for the proposed model. The case study on the Tennessee Eastman Process is used to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, a data-driven multiblock concurrent projection to latent structures (CPLS) method is proposed for monitoring large-scale manufacturing lines, particularly for cold rolling continuous annealing processes (CAPs) fault diagnosis. The proposed method provides decentralized process monitoring and helps localize faults in both input variables and output variables concurrently. First, the CPLS-based process monitoring method is briefly reviewed. Second, a multiblock CPLS algorithm, which incorporates process block partition, is proposed to diagnose faults relevant to process inputs or outputs with a decentralized structure. For the CAP line application, tension-specific variations, roll-specific variations, and tension–roll covariations are analyzed in each partitioned block. Furthermore, within the roll-specific subspace of an abnormal block, a delay-alignment scheme based on strip transportation delay is proposed to diagnose defective processing materials. [ABSTRACT FROM PUBLISHER]

Process monitoring and fault diagnosis of the continuous annealing process lines (CAPLs) have been a primary concern in industry. Stable operation of the line is essential to final product quality and continuous processing of the upstream and downstream materials. In this paper, a multilevel principal component analysis (MLPCA)-based fault diagnosis method is proposed to provide meaningful monitoring of the underlying process and help diagnose faults. First, multiblock consensus principal component analysis (CPCA) is extended to MLPCA to model the large scale continuous annealing process. Secondly, a decentralized fault diagnosis approach is designed based on the proposed MLPCA algorithm. Finally, experiment results on an industrial CAPL are obtained to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades. The scope of the problem is described with reference to the scale and complexity of industrial process operations, where multi-level hierarchical optimization and control are necessary for efficient operation, but are also prone to hard failure and soft operational faults that lead to economic losses. Commonly used multivariate statistical tools are introduced to characterize normal variations and detect abnormal changes. Further, diagnosis methods are surveyed and analyzed, with fault detectability and fault identifiability for rigorous analysis. Challenges, opportunities, and extensions are summarized with the intent to draw attention from the systems and control community and the process control community. [Copyright &y& Elsevier]

Abstract: In process monitoring, several diagnosis methods have been used for fault diagnosis. These methods have been developed from different backgrounds and considerations. In this paper, five existing diagnosis methods are analyzed and generalized. It is shown that they can be unified into three general methods, making the original diagnosis methods special cases of the general ones. Also, a new form of relative contributions is proposed. An analysis of the diagnosability shows that some diagnosis methods do not guarantee correct diagnosis even for simple sensor faults with large magnitudes. For faults with modest fault magnitudes, Monte Carlo simulation is applied to compare the performance of the diagnosis methods. [Copyright &y& Elsevier]

Abstract: This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, and a combined index . Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes. [Copyright &y& Elsevier]

The purposes of multivariate statistical process control (MSPC) are to improve process operations by quickly detecting when process abnormalities have occurred and diagnosing the sources of the process abnormalities. In the area of semi-conductor manufacturing, increased yield and improved product quality result from reducing the amount of wafers produced under suboptimal operating conditions. This paper presents a complete MSPC application method that combines recent contributions to the field, including multiway principal component analysis (PCA), recursive PCA, fault detection using a combined index, and fault contributions from Hotelling's T2 statistic. In addition, a method for determining multiblock fault contributions to the combined index is introduced. The effectiveness of the system is demonstrated using postlithography metrology data and plasma stripper processing tool data. [ABSTRACT FROM AUTHOR]

The practitioners are concerned with strip-thickness relevant faults of steel-making cold-rolling continuous annealing process (CAP) which is a typical dynamic nonlinear process. In this paper, a novel data-driven dynamic concurrent kernel canonical correlation analysis (DCKCCA) approach is proposed for the diagnosis of the CAP strip thickness relevant faults. First, a DCKCCA algorithm is proposed to capture dynamic nonlinear correlations between strip thickness and process variables. Strip thickness specific variations, process-specific variations, and thickness-process covariations are monitored respectively. Secondly, a multi-block extension of DCKCCA is designed to compute the contributions according to block partition of lagged variables, in order to help localize faults relevant to abnormal strip thickness. Finally, the proposed methods are illustrated by the application to a real continuous annealing process. [ABSTRACT FROM AUTHOR]

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]