Process Modeling and Prediction With Large Number of High-Dimensional Variables Using Functional Regression

Gahrooei, Mostafa Reisi; Paynabar, Kamran; Pacella, Massimo; Shi, Jianjun

doi:10.1109/tase.2019.2941167

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…One possible extension of the MOSS is to multiple response regression under the non-parametric estimation framework [ 55 ]. Next, the spatial process variables and quality responses, such as the thermal video and 3d profile of the product, can be incorporated into the MOSS to reasonably quantify the spatio-temporal relationship contained in both process variables and quality variables [ 56 , 57 ]. Finally, the monitoring and control strategy can also be integrated with the MOSS in a real-time manner to effectively detect the anomaly event during the fabrication process, and further improve process reliability and reduce process variation [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

Wang

Jin

2021

Sensors

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Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

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Section: Discussionmentioning

confidence: 99%

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

Wang

Jin

2021

Sensors

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“…This method should perform variable selection to distinguish which inputs are most informative in the original measurement domain, i.e., a method capable of sensor screening by selecting the most informative variable inputs. A recent example in the literature of a statistical method to perform sensor screening, and to generate predictions, is reported in [ 41 ] concerning a case study related to the monitoring of an internal combustion engine through a large number of sensor signals.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints

Pacella

Papadia

2020

Sensors

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This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection.

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“…48 Lock 13 provides a Tensor-on-Tensor Regression method with a CP structure of the regression parameters using least squares, and Llosa et al 49 use the Tucker structure of the coefficients. Gahrooei et al 50 propose a functional regression method in which a high-dimensional response is estimated and predicted by a set of informative and non-informative high-dimensional covariates through a set of low-dimensional smooth basis functions. An application of the proposed functional regression method are joint motion trajectories.…”

Section: Tensor-based Methods For (Motion) Predictionmentioning

confidence: 99%

“…use the Tucker structure of the coefficients. Gahrooei et al 50 . propose a functional regression method in which a high‐dimensional response is estimated and predicted by a set of informative and non‐informative high‐dimensional covariates through a set of low‐dimensional smooth basis functions.…”

Section: Related Workmentioning

confidence: 99%

A Tensor‐based Regression Approach for Human Motion Prediction

Wedenig

Chris

Kleb

2022

Quality & Reliability Eng

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Collaborative robotic systems will be a key enabling technology for current and future industrial applications. The main aspect of such applications is to guarantee safety for humans. To detect hazardous situations, current commercially available robotic systems rely on direct physical contact to the co-working person. To further advance this technology, there are multiple efforts to develop predictive capabilities for such systems. Using motion tracking sensors and pose estimation systems combined with adequate predictive models, potential episodes of hazardous collisions between humans and robots can be predicted. Based on the provided predictive information, the robotic system can avoid physical contact by adjusting speed or position. A potential approach for such systems is to perform human motion prediction with machine learning methods like artificial neural networks (NNs). In our approach, the motion patterns of past seconds are used to predict future ones by applying a linear Tensor-on-Tensor Regression model, selected according to a similarity measure between motion sequences obtained by dynamic time warping (DTW). For test and validation of our proposed approach, industrial pseudo assembly tasks were recorded with a motion capture system, providing unique traceable Cartesian coordinates (𝑥, 𝑦, 𝑧) for each human joint. The prediction of repetitive human motions associated with assembly tasks, whose data vary significantly in length and have highly correlated variables, has been achieved in real time.

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Process Modeling and Prediction With Large Number of High-Dimensional Variables Using Functional Regression

Cited by 13 publications

References 29 publications

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints

A Tensor‐based Regression Approach for Human Motion Prediction

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