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PLS is widely used in the quality control process system, but it has poor capability in some strong local nonlinear system for fault diagnosis. To enhance the monitoring ability of such type fault, a novel statistical model based on global plus local projection to latent structures (GPLPLS) is proposed. Firstly, the characteristics and nature of quality-related global and local partial least squares...
The objective of this paper is to address a new method based on trend extraction for isolating faults in the nonstationary and nonlinear processes. Firstly, a concise review of the traditional methods for fault isolation based on Hotelling statistic are introduced, a rigorous analysis of their weaknesses, especially the smearing (coupling) phenomena, is provided, and the possible handling strategies...
Industries and industry-wide standards of credit is littery at present, there is not uniform standard of credit system and the social evaluation, application and supervision system. And large credit is lossing in the process of credit evaluation and it cannot have better credit rating, including personal and business groups. Application of dimension analysis in this paper from the other side, namely...
"Outliers" or "anomalous data points" occur frequently in practice and can have devastating effects on process data analysis, empirical modeling, or controller implementation. This paper briefly examines the nature of these anomalous data points, their influence, and three possible approaches to dealing with them. One of the key points of this paper is that effective procedures...
In this article, a model migration strategy based on subspace separation is proposed for process monitoring by taking advantage of common information between an old process and a new process. Firstly, a global basis vector is extracted and deemed to enclose the cross-set similar correlations. Then two different subspaces are separated from each other in the new dataset. The kernel principal component...
Statistical process control techniques have found widespread application in industry for process improvement and for estimating process parameters or determining capability. Unfortunately, the assumption of uncorrelated or independent observations is not even approximately satisfied in some manufacturing processes. All manufacturing processes are driven by inertial elements, and the frequency of sampling...
Process capability analysis is conducted assuming that the process under study is in statistical control and independent observations are generated over time. However, in practice it is very common to come across process which, due to their inherent natures, generate autocorrelated observations. An approach that has proved useful in dealing with autocorrelated data is to directly model the correlative...
Statistical process control (SPC) chart for individual observation helps to understand the state of control, stability, and capability of a process. However, most of the typical control chart suggested for individual observation is primarily based on the assumption that the process data are independent and normally distributed. This assumption of independency and normality is generally violated in...
This study proposed an approach which simultaneously considers the properties of cost and quality by minimum value of expected cost per hour which is restricted by maximum value of type I error (αU) and minimum value of power (pL) to determine three parameters (including sample size, sampling interval between successive samples, and the control limits) when an x bar chart supervises a manufacturing...
Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a model ARMA(p,q), for calculate the run length distribution (RLD),...
The paper deals with the design of a data driven soft sensor, able to estimate propylene percentage in the bottom flow of a Propylene Splitter showing seasonal variations. Experimental data have been collected in a refinery in Sicily. The soft sensor is intended to replace the online analyzer during maintenance, in order to guarantee the desired plant performance. In order to take into account seasonal...
In order to promote the information-based development of the textile industry, and improve the production management, we develop a production monitoring and decision system via network technology, database technology, communication technology and transaction mechanism, and propose a Multi-Agent management decision and data analysis model. For all machines' production data and operating status, system...
The procedure to accommodate continuous data with autocorrelation is to use control charts residuals which are adjusted by time series methods, and when we analyze discrete data with autocorrelation is analyzed, the Poisson distribution is often employed. This paper shows the application of statistic quality control (SQC) to the sand foundry in the Industry FUNDIMISA, in Santo Angelo (RS/Brasil)....
In this article, we propose an optimal bivariate field chart to monitor two correlated characteristics of count data. This chart is based on optimization of bivariate Poisson confidence interval and projection of bivariate Poisson data in Poisson field. Both a real case study and simulations present improved performance of our proposed algorithm. Our experimental results show improved rate of average...
Although linear regression is a simple and useful method to build process models, they do not always function well in practice due to not only changes in process characteristics but differences of specifities between the equipments when multiple equipments are operated in parallel. To cope with them, the correlation between variables should be considered. In the present work, a new pattern recognition...
Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a Dynamic Linear Model, to calculate the run length distribution...
For several decades, the output from semiconductor manufacturers has been high volume products with process optimisation being continued throughout the lifetime of the product to ensure a satisfactory yield. However, product lifetimes are continually shrinking to keep pace with market demands. Furthermore there is an increase in dasiafoundrypsila business where product volumes are low; consequently...
The challenges of deriving early-adopter competitive advantage, even with fabless access to process technology, through leveraging features offered by the advanced, and possibly disruptive, process technologies in real SoC products, are outlined. A structured methodology for addressing these challenges, and bridging the gap between process and design, sufficiently early in the development cycle to...
In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the...
Cell-based clustering method using cell data structure, so all operations are clustering on the cell. Based on the processing of the observation data clustering, algorithms about data fusion and objective tracking are discussed. Matrixes of filter formula, such as the observation transfer-matrix, observation noise covariance matrix, the initial value of estimation covariance matrix, and observation...
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