scholarly journals Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach

2002 ◽  
Vol 124 (2) ◽  
pp. 313-322 ◽  
Author(s):  
Yu Ding ◽  
Dariusz Ceglarek ◽  
Jianjun Shi
Keyword(s):  
State Space ◽  
State Space Model ◽  
Principal Component ◽  
The State ◽  
Manufacturing Processes ◽  
Matrix Perturbation ◽  
Mapping Procedure ◽  
Space Model ◽  
Multistage Manufacturing ◽  
Single Station

This paper presents a methodology for diagnostics of fixture failures in multistage manufacturing processes (MMP). The diagnostic methodology is based on the state-space model of the MMP process, which includes part fixturing layout geometry and sensor location. The state space model of the MMP characterizes the propagation of fixture fault variation along the production stream, and is used to generate a set of predetermined fault variation patterns. Fixture faults are then isolated by using mapping procedure that combines the Principal Component Analysis (PCA) with pattern recognition approach. The fault diagnosability conditions for three levels: (a) within single station, (b) between stations, and (c) for the overall process, are developed. The presented analysis integrates the state space model of the process and matrix perturbation theory to estimate the upper bound for isolationability of fault pattern vectors caused by correlated and uncorrelated noises. A case study illustrates the proposed method.

Method of Identifying Key Quality Characteristics in Multistage Manufacturing Process Based on PLSR

2012 ◽  
Vol 217-219 ◽  
pp. 2580-2584 ◽  
Author(s):  
Ning Wang ◽  
Ji Chao Xu ◽  
Jian Feng Yang
Keyword(s):  
State Space ◽  
Manufacturing Process ◽  
State Space Model ◽  
Quality Characteristics ◽  
The State ◽  
Quality Analysis ◽  
Least Squares Regression ◽  
Space Model ◽  
Multistage Manufacturing ◽  
And Control

To improve the existing methods of identifying the key quality characteristics in multistage manufacturing process, the partial least squares regression (PLSR) method is combined with the state space model that a new method of identifying the key quality characteristics in multistage manufacturing process based on PLSR is proposed. According to the feature of multistage manufacturing process, the state space model is introduced to build the key quality characteristics identifying model for multistage manufacturing process, using the PLSR method to solve the problem of the quality characteristics such as multicollinearity, do model analyzing and identify the key quality characteristics. At last, the cigarette production process is presented as an example to introduce the application of this method. The result shows that this method can not only identify the key quality characteristics in multistage manufacturing process, but also establish the model of output quality effecting of all levels on the final product quality and its quality characteristics relationship, which reflect the structure of the multistage manufacturing process and causal relationship between quality characteristics at all process levels, provide the basis for quality analysis and control in multistage manufacturing process.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

Outlier detection in the state space model

1994 ◽  
Vol 20 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Siddhartha Chib ◽  
Ram C. Tiwari
Keyword(s):  
State Space ◽  
Outlier Detection ◽  
State Space Model ◽  
The State ◽  
Space Model

An empirical study-based state space model for multilayer overlay errors in the step-scan lithography process

RSC Advances ◽  
2015 ◽  
Vol 5 (126) ◽  
pp. 103901-103906 ◽  
Author(s):  
Fuyun He ◽  
Zhisheng Zhang
Keyword(s):  
Empirical Study ◽  
State Space ◽  
Integrated Circuit ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
State Space Model ◽  
Key Factors ◽  
Space Model ◽  
Lithography Process ◽  
Multistage Manufacturing

In semiconductor manufacturing, the multilayer overlay lithography process is a typical multistage manufacturing process; one of the key factors that restrict the reliability and yield of integrated circuit chips is overlay error between the layers.

Establishment of State Variable Model of Aeroengine with Improved Least Square Fitting

2010 ◽  
Vol 40-41 ◽  
pp. 27-33 ◽  
Author(s):  
Yi Hui Lin ◽  
Hai Bo Zhang
Keyword(s):  
Differential Equations ◽  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
Least Square ◽  
Variable Model ◽  
Space Model ◽  
Fitting Method ◽  
Linear Differential

The method of state space model fitting is carried out by using the linear relation of the variable of the differential equations and separating the steady process and instant process to eliminate the steady errors course by instant errors. The improved fitting method is without solving the linear differential equations or using any iterative methods. The coefficient of the state space model can be solve simply using matrix operation under the premise of high accuracy, so it has a higher computational efficiency than former least square method. And this method can also be used with other fitting method. Finally, to illustrate the validity and accuracy of the improved method, a small perturbation state space model of a certain turboshaft engine model has been established by this method, and the simulation result between state space model and nonlinear model was also compared. Also, the state space model could be applied to fault diagnosis and control system design for aeroengines.

scholarly journals A Stochastic and State Space Model for Tumour Growth and Applications

2009 ◽  
Vol 10 (2) ◽  
pp. 117-138 ◽  
Author(s):  
Wai-Yuan Tan ◽  
Weiming Ke ◽  
G. Webb
Keyword(s):  
State Space ◽  
Stochastic System ◽  
Tumour Growth ◽  
Deterministic Model ◽  
State Space Model ◽  
Tumour Cells ◽  
The State ◽  
System Model ◽  
State Variables ◽  
Space Model

We develop a state space model documenting Gompertz behaviour of tumour growth. The state space model consists of two sub-models: a stochastic system model that is an extension of the deterministic model proposed by Gyllenberg and Webb (1991), and an observation model that is a statistical model based on data for the total number of tumour cells over time. In the stochastic system model we derive through stochastic equations the probability distributions of the numbers of different types of tumour cells. Combining with the statistic model, we use these distribution results to develop a generalized Bayesian method and a Gibbs sampling procedure to estimate the unknown parameters and to predict the state variables (number of tumour cells). We apply these models and methods to real data and to computer simulated data to illustrate the usefulness of the models, the methods, and the procedures.

scholarly journals Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1445
Author(s):  
Rodi Lykou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
A Priori ◽  
State Space Model ◽  
Computational Cost ◽  
The State ◽  
Observation Equation ◽  
Basic Algorithm ◽  
Space Model ◽  
Observational Errors

Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.

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