An algorithm for robust multiblock partial least squares predictive modelling

Self Cite

Higher order data are commonly encountered in the domain of chemometrics, often generated by advanced analytical instruments. Due to the multilinear nature of the data, higher order data require different regression approaches compared with traditional two‐way data for predictive modelling. The main aim is usually to extract the rich multilinear information, which is often lost if the data are simply analysed in unfolded form. A common algorithm for multilinear predictive modelling is N‐way partial least squares (NPLS). However, a limitation of NPLS is that it inherently does not handle outlying observations, which can be detrimental to the model. This work presents a new robust multilinear predictive modelling approach based on iterative down‐weighting of the outlier observations in both predictor and response space. A key benefit of the method is that it only requires a single extra parameter to tune. In this work, the algorithm is described, and the method is demonstrated on three real multilinear data sets. In all cases, the presented method outperformed the traditional NPLS modelling regarding the root mean squared error of prediction.

Section: Algorithmmentioning

confidence: 99%

Section: Comments On the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Iterative re‐weighted multilinear partial least squares modelling for robust predictive modelling

Mishra,

Liland

2023

Self Cite

Distributed statistical process monitoring based on block‐wise residual generator

Tong,

Zhou,

Qian

et al. 2023

The increasing scale of modern chemical plants keeps popularizing investigation as well as application of distributed process monitoring approaches. With a goal of directly quantifying the normal relations between different blocks divided from the whole process, a novel multi‐block modeling strategy called block‐wise residual generator is proposed, which trains a residual generator for each block through using the partial least squares algorithm with single one output, so that the relation between the corresponding block and the others is quantified as a regression model in a block‐wise manner. The deviations caused by the abnormal samples to the normal relations quantified for different blocks could thus be efficiently captured by the residuals generated from the block regression models, which then provide sensitive information for fault detection and contribution‐based fault diagnosis. Moreover, the proposed method is applicable for both disjoint and overlapped block divisions, and the direct consideration of individually quantifying relations between different blocks can always guarantee its salient monitoring performance, as validated through comparisons with classical distributed process monitoring methods.

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Hu,

Chang

2023

With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the online monitored samples after data augmentation, so that the corresponding inconsistency within the given consecutive samples could be timely and adaptively uncovered. Instead of expressing the time‐serial relationship that generally accepted by the normal samples in the given dataset, solving the objective function designed for JITLAR2G in a just‐in‐time manner can adaptively and correspondingly seek but only one projecting vector as well as coefficient vector to generate residual, which points to the potential inconsistency inherited in the monitored samples, for the sole purpose of fault detection. As demonstrated through comparisons, the proposed JITLAR2G model can consistently guarantee its effectiveness, in terms of reducing both false alarm rate and missed alarm rate, for dynamic process monitoring, the salient performance achieved by the proposed JITLAR2G‐based method in contrast to the counterparts can be always confirmed.