Search

Although the Lasso method has been popular for variable selection in regression modeling, it has been known to yield very different model structures with minor perturbations of the training data. A consequence is that, when cross-validation (CV) is used to determine the hyperparameter λ , seemingly heterogeneous model structures among the CV-folds are resulted for the same λ. In this paper, we propose a new stable Lasso method for model structure learning of static and dynamic models. We begin with building consensus Lasso models with a grid of λ values using all training data. Then the CV-fold models are optimized to conform with the consensus model structures with a modified Lasso objective. In addition, we propose a stable criterion that uses CV errors jointly with a stability measure to select the most stable model with near minimum CV errors. The proposed method is applied to inferential modeling of a chemical plant at DOW Chemical and dynamic modeling of an industrial boiler. • A stable Lasso algorithm is proposed to select variables for inferential sensor modeling. • The algorithm enhances consistent structures in the cross-validation step. • A stable selection criterion of MSE jointly the Jaccard stability measure is proposed. • Successful case studies are presented on inferential modeling of two industrial processes. [ABSTRACT FROM AUTHOR]

Shape accuracy is an important quality measure of finished parts built by Additive Manufacturing (AM) processes. Previous work has established a generic and prescriptive methodology to represent, predict and compensate in-plane (x – y plane) shape deviation of AM built products using a limited number of test cases. However, extension to the out-of-plane (z-plane) shape deviation faces a major challenge due to intricate inter-layer interactions and error accumulation. One direct manifestation of such complication is that products of the same shape exhibit different deviation patterns when varying product sizes. This work devises an economic experimental plan and a data analytical approach to model out-of-plane deviation for improving the understanding of inter-layer interactions using a small set of training shapes. The key strategy is to discover the transition of deviation patterns from a smaller shape with fewer layers to a bigger one with more layers. This transition is established through the effect equivalence principle, which enables the model predicting a smaller shape to digitally "reproduce" the bigger shape by identifying the equivalent amount of design adjustment. In addition, a Bayesian approach is established to infer the deviation models capable of predicting deviation of complex shapes along the z-direction. Furthermore, prediction and compensation of out-of-plane deviation for two-dimensional freeform shapes are accomplished with experimental validation in a stereolithography process. [ABSTRACT FROM AUTHOR]

Abstract: Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper. [Copyright &y& Elsevier]

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]

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]

Principal component analysis (PCA) has been widely used for monitoring complex industrial processes with multiple variables and diagnosing process and sensor faults. The objective of this paper is to develop a new subspace identification algorithm that gives consistent model estimates under the errors-in-variables (EIV) situation. In this paper, we propose a new subspace identification approach using principal component analysis. PCA naturally falls into the category of EIV formulation, which resembles total least squares and allows for errors in both process input and output. We propose to use PCA to determine the system observability subspace, the A, B, C, and D matrices and the system order for an EIV formulation. Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices. The proposed subspace identification method is demonstrated using a simulated process and a real industrial process for model identification and order determination. For comparison the MOESP algorithm and N4SID algorithm are used as benchmarks to demonstrate the advantages of the proposed PCA based subspace model identification (SMI) algorithm. [Copyright &y& Elsevier]

The development of industry has led to the big data revolution in manufacturing industry, which has greatly improved production quality and efficiency. A accuracy prediction model is established by using deep learning method, especially short and long term memory (LSTM). However, the application field of LSTM is usually very limited, and the interpretability of LSTM is weak. In this paper, a novel bidirectional DiPLS based LSTM algorithm (BiDiPLS-LSTM) is proposed to solve this problem. First, DiPLS is used to process the forward time series data to obtain the most predictable dynamic latent variable (DLV) of the target variable. At the same time, DiPLS method is also used to process the reverse time series data and extract the reverse DLV. Then, the forward and reverse dynamic features of the same location will form new input data, and this new input data will be input into the LSTM network to realize the prediction of time series data. In order to verify the effectiveness of the proposed BiDiPLS-LSTM method, the real 660 MW coal-fired boiler process data is used, and the emission of oxides of nitrogen (NO x) is regarded as the output. The results show that the extracted dynamic features can be used to build a very accuracy model, and the results outperform the traditional LSTM method, the PLS-LSTM method, the unidirectional DiPLS-LSTM method, and traditional RNN method in the comparison. For the first time, we used the bidirectional dynamic features of the input data as the real input to predict the output, which will obtain a good prediction effect. What is more, the use of dynamic latent variables made the result more explanatory. • The receive field of LSTM is usually very limited, and the interpretability is weak. • DiPLS can extract dynamic features between input and output. • DiPLS can improve the interpretability of LSTM extraction. • a novel bidirectional DiPLS based LSTM algorithm is proposed in this paper. • Application results confirm the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

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]