Fault Localization Analysis for Multiple Fault Diagnosis in Multi-Station Assembly Systems

2007 ◽  
Author(s):  
Posheng Tsai ◽  
Kamal Mannar ◽  
Darek Ceglarek
Keyword(s):  
Manufacturing Systems ◽  
Large Scale ◽  
Fault Localization ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Data Driven ◽  
Multiple Fault ◽  
Critical Measure ◽  
The Impact

Modern large scale multi-station manufacturing systems require effective variation reduction to improve the final assembly dimensional quality. One critical measure is to diagnose the fault in the process using knowledge-based root cause identification, which can be very challenging due to the complexity of the system. The paper investigates the need of data-driven fault localization to enhance the diagnosability within the context of multiple-fault scenario(s) in multi-station assembly processes where multivariate measurements are used. The paper proposes three types of fault-signal transmission in assembly system and illustrates the nature of structured noise. Moreover, the impact of structured noise on the diagnosability is illustrated on two major fault isolation methods, namely, Principal Component Analysis and Independent Component Analysis. We then propose to use data-driven fault localization to reduce the structured noise effect and enhance the diagnosability. A simulation case study based on automotive panel assembly model is provided to illustrate the impact of structured noise and the need for data-driven localization.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

2021 ◽  
Vol 13 (10) ◽  
pp. 5359
Author(s):  
Afrika Onguko Okello ◽  
Jonathan Makau Nzuma ◽  
David Jakinda Otieno ◽  
Michael Kidoido ◽  
Chrysantus Mbi Tanga
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Principal Component ◽  
Farm Income ◽  
Purchase Decisions ◽  
Wealth Status ◽  
Feed Formulation ◽  
Alternative Protein ◽  
Livestock Products ◽  
The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

2022 ◽  
pp. 146808742110707
Author(s):  
Aran Mohammad ◽  
Reza Rezaei ◽  
Christopher Hayduk ◽  
Thaddaeus Delebinski ◽  
Saeid Shahpouri ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Emission Models ◽  
Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

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