An optimization of on-line monitoring of simple linear and polynomial quality functions

This research aims to introduce a number of contributions for enhancing the statistical performance of some of Phase II linear and polynomial profile monitoring techniques. For linear profiles the idea of variable sampling size (VSS) and variable sampling interval (VSI) have been extended from multivariate control charts to the profile monitoring framework to enhance the power of the traditional T^2 chart in detecting shifts in linear quality models. Finding the optimal settings of the proposed schemes has been formulated as an optimization problem solved by using a Genetic Approach (GA). Here the average time to signal (ATS) and the average run length (ARL) are regarded as the objective functions, and ATS and ARL approximations, based on Markov Chain Principals, are extended and modified to capture the special structure of the profile monitoring. Furthermore,the performances of the proposed control schemes are compared with their fixed sampling counterparts for different shift levels in the parameters. The extensive comparison studies reveal the potentials of the proposed schemes in enhancing the performance of T^2 control chart when a process yields a simple linear profile. For polynomial profiles, where the linear regression model is not sufficient, the relationship between the parameters of the original and orthogonal polynomial quality profiles is considered and utilized to enhance the power of the orthogonal polynomial method (EWMA4). The problem of finding the optimal set of explanatory variable minimizing the average run length is described by a mathematical model and solved using the Genetic Approach. In the case that the shift in the second or the third parameter is the only shift of interest, the simulation results show a significant reduction in the mean of the run length distribution of the EWMA4 technique

On monitoring of multiple non-linear profiles

Most state-of-the-art profile monitoring methods involve studies of one profile. However, a process may contain several sensors or probes that generate multiple profiles over time. Quality characteristics presented in multiple profiles may be related multiple aspects of product or process quality. Existing charting methods for simultaneous monitoring of each multiple profile may result in high false alarm rates. Or worse, they cannot correctly detect potential relationship changes among profiles. In this study, we propose two approaches to detect process shifts in multiple non-linear profiles. A simulation study was conducted to evaluate the performance of the proposed approaches in terms of average run length under different process shift scenarios. Pros and cons of the proposed methods are discussed. A guideline for choosing the proposed methods is introduced. In addition, a hybrid method combining the salient points of both approaches is explored. Finally, a real-world data-set from a vulcanisation process is used to demonstrate the implementation of the proposed methods

Bayesian separation of spectral sources under non-negativity and full additivity constraints

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and non-negativity of sources and mixing coefficients. A Bayesian estimation approach based on Gamma priors was recently proposed to handle the non-negativity constraints in a linear mixture model. However, incorporating the full additivity constraint requires further developments. This paper studies a new hierarchical Bayesian model appropriate to the non-negativity and sum-to-one constraints associated to the regressors and regression coefficients of linear mixtures. The estimation of the unknown parameters of this model is performed using samples generated using an appropriate Gibbs sampler. The performance of the proposed algorithm is evaluated through simulation results conducted on synthetic mixture models. The proposed approach is also applied to the processing of multicomponent chemical mixtures resulting from Raman spectroscopy.Comment: v4: minor grammatical changes; Signal Processing, 200

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

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A cross sectional analysis of the association between FGF19 tumor expression and serum AFP levels in advanced HCC patients

PURPOSE: HCC is a complicated disease with high mortality rates and limited treatment options. No universal clinical or molecular classification established to inform better treatment options. There has been very limited success in determining a molecular profile that represent valid drivers in HCC patients and thus no targeted agents have obtained marketing approval. However, emerging data suggest the FGF19-pathway as a HCC driver and a potential therapeutic target. This research study aims to investigate whether the HCC prognostic risk factor, serum AFP, is predictive of FGF19 protein expression as assessed by immunohistochemistry in advanced HCC patients. METHODS: A cross-sectional analysis was performed from baseline data collected in a Phase 1 study conducted at various centers across the US, EU, and Asia. Only advanced HCC patients with adequate liver function were eligible for enrollment. Demographic data, detailed history of HCC, and any prior treatments or surgeries were recorded. Baseline laboratory values and prognostic factors including performance status (ECOG), lab values (i.e. bilirubin, albumin), and the number, size and biomarker status of the tumor(s) were collected. Differences between groups were assessed by t test, or Chi-square test, as appropriate. Multivariate logistic stepwise regression analyses were performed including all parameters with highly significant correlations in the multivariate analysis. RESULTS: Only AFP, metastatic disease, and prior surgery met the criteria to be incorporated into the final model. Results indicated that high AFP had a statistically significant (p-value = .01) positive association (Wald chi-square statistic = 6.601) with positive FGF19 IHC status. The odds ratio for being FGF19 IHC+ was 12.216 among the high AFP subjects as compared to low AFP subjects, and also statistically significant but had a very wide 95% confidence interval (1.811, 82.79). CONCLUSIONS: The results indicated that HCC patients with high serum AFP levels have a twelve fold higher chance of having a positive FGF19 IHC status than those with low AFP levels. Further studies are warranted in order to replicate the data in a larger sample size to understand future clinical implications once treatment options become available for FGF19 IHC positive patients

Profile control chart based on maximum entropy

Monitoring a process over time is so important in manufacturing processes to reduce the wastage of money and time. The purpose of this article is to monitor profile coefficients instead of a process mean. In this paper, two methods are proposed for monitoring the intercept and slope of the simple linear profile, simultaneously. The first one is linear regression, and another one is the maximum entropy principle. A simulation study is applied to compare the two methods in terms of the second type of error and average run length. Finally, two real examples are presented to demonstrate the ability of the proposed chart

Scoping biological indicators of soil quality Phase II. Defra Final Contract Report SP0534

This report presents results from a field assessment of a limited suite of potential biological indicators of soil quality to investigate their suitability for national-scale soil monitoring