Regression Control Chart Research Articles

Support Vector Regression control charts for multivariate nonlinear autocorrelated processes

Statistical process control charts are one of the most widely used techniques in industry and laboratories that allow monitoring of systems against faults. To control multivariate processes, most classical charts need to model process structure and assume that variables are linearly and independently distributed. This study proposes to use a nonparametric method named Support Vector Regression to construct several control charts that allow monitoring of multivariate nonlinear autocorrelated processes. Also although most statistical quality control techniques focused on detecting mean shifts, this research investigates detection of different parameter shifts. Based on simulation results, the study shows that, with a controlled robustness, the charts are able to detect the different applied disturbances. Moreover in comparison to Artificial Neural Networks control chart, the proposed charts are especially more effective in detecting faults affecting the process variance.

Fuzzy Regression Control Chart Based on α-cut Approximation

The fuzzy regression control chart is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this study, the theoretical structure of the “α-level fuzzy midrange for α-cut fuzzy -regression control chart” is proposed for triangular (TFN) and trapezoidal (TrFN) membership functions. In addition, the real world application is evaluated with fuzzy regression control charts and fuzzy [Rtilde] control charts for the triangular membership function.

Fuzzy Regression Control Chart Based on α-cut Approximation

Fuzzy Regression Control Chart Based on α-cut Approximation

SIMULATED SHEWHART CONTROL CHART FOR MONITORING VARIANCE COMPONENTS

Shewhart control charts based on the simulation method are proposed for monitoring separate variance components of the single-factor random effect model. Monte Carlo simulation results show that the proposed control charts have competitive performance relative to the approximate Shewhart regression control charts (ARSCCs) proposed by Chang and Gan2 in terms of the average run length (ARL). Compared with the ARSCCs, our control charts can be constructed easily with less sample resources. The application of our proposed method is illustrated with two examples.

Run-Length Performance of Regression Control Charts with Estimated Parameters

The regression control chart is an effective statistical process control (SPC) tool in situations where the output characteristic of interest is affected by an external covariate. It has many applications, including monitoring and diagnosing multistage processes. Its basic purpose is to remove the influence of the covariat using regression adjustment and then apply a regular control scheme to the regression residual. In practice, the regression model relating the output and the covariate is rarely known and needs to be estimated. Little is known about the performance of the regression control chart when the true parameters are replaced with their estimates. In this paper, the run length performance of regression control charts with estimated parameters is studied. Two types of regression control charts are considered: a Shewhart control for regression residuals, and an exponentially weighted moving average (EWMA) control for regression residuals.

MIXER VISCOMETRY to CHARACTERIZE FLUID FOODS WITH LARGE PARTICULATES

ABSTRACTMixer viscometry is an effective means of characterizing fluids containing particulates. an interrupted helical screw impeller used to determine the flow behavior of fresh concrete was scaled‐down to analyze fluid foods with large particulates. the mixer viscometer constant was determined to be 1.6 rad−1 by the matching viscosity method. Large particulate food materials successfully investigated in this study included cream corn, salsa, smooth pasta sauce and chunky pasta sauce.Apparent viscosity curves were constructed from torque and angular velocity data. Testing materials exhibited shear‐thinning behavior and temperature dependence. Analysis revealed K and n values ranging from 10.1 Pa sn to 40.3 Pa sn and 0.17 to 0.36, respectively. Regression control charts, based on apparent viscosity and average shear rate data were constructed for each product. A protocol is presented for producing and utilizing these charts as a statistical quality control tool for industry.

A Multivariate Control Chart for Expected Changes Over Time

A new procedure is proposed that merges the methodologies of regression control charts and multivariate control charts. The simulation of this procedure shows that this new control chart can be very effective in detecting even modestly larger than expected changes in several monitored variables which are subject to some naturally occurring changes over time.

Gráfico de controle de regressão aplicado na monitoração de processos

Esta pesquisa tem por objetivo empregar o gráfico de controle de regressão, como ferramenta de controle estatístico, para monitorar processos produtivos, onde uma variável de estado, que seja de interesse, possa ser expressa como função de uma variável de controle. Existem vários estudos sobre o controle de variáveis em processos produtivos, mas, na maioria das vezes, são em relação ao controle de cada variável, separadamente, não podendo ser utilizados para um estudo comparativo. Esta pesquisa, portanto, apresenta uma técnica eficiente no controle simultâneo de variáveis correlacionadas.