Affine invariant signed-rank multivariate exponentially weighted moving average control chart for process location monitoring

Multivariate statistical process control (SPC) charts for detecting possible shifts in mean vectors assume that data observation vectors follow a multivariate normal distribution. This assumption is ideal and seldom met. Nonparametric SPC charts have increasingly become viable alternatives to parametric counterparts in detecting process shifts when the underlying process output distribution is unknown, specifically when the process measurement is multivariate. This study examined a new nonparametric signed-rank multivariate exponentially weighted moving average type (SRMEWMA) control chart for monitoring location parameters. The control chart was based on adapting a multivariate spatial signed-rank test. The test was affine-invariant and the weighted version of this test was used to formulate the charting statistic by incorporating the exponentially weighted moving average (EWMA) scheme. The test\u27s in-control (IC) run length distribution was examined and the IC control limits were established for different multivariate distributions, both elliptically symmetrical and skewed. The average run length (ARL) performance of the scheme was computed using Monte Carlo simulation for select combinations of smoothing parameter, shift, and number of p-variate quality characteristics. The ARL performance was compared to the performance of the multivariate exponentially weighted moving average (MEWMA) and Hotelling T2. The control charts for observation vectors sampled the multivariate normal, multivariate t, and multivariate gamma distributions. The SRMEWMA control chart was applied to a real dataset example from aluminum smelter manufacturing that showed the SRMEWMA performed well. The newly investigated nonparametric multivariate SPC control chart for monitoring location parameters--the Signed-Rank Multivariate Exponentially Weighted Moving Average (SRMEWMA)--is a viable alternative control chart to the parametric MEWMA control chart and is sensitive to small shifts in the process location parameter. The signed-rank multivariate exponentially weighted moving average performance for data from elliptically symmetrical distributions is similar to that of the MEWMA parametric chart; however, SRMEWMA\u27s performance is superior to the performance of the MEWMA and Hotelling\u27s T2 control charts for data from skewed distributions

On the Development of the Self Starting Double Multivariate Exponentially Weighted Moving Average And Covariance Control Chart

Control charts are an important element for monitoring production processes in a wide array of industries. A strong performing control chart is one that responds quickly to undesirable changes in a production process. This work demonstrates the expansion of Multivariate Exponentially Weighted Moving Average and Covariance (MEWMAC) control chart to be doubly weighted in efforts to improve performance by reducing out of control run lengths (ARL1) when changes occur in either the process mean vector or covariance matrix. Metric derivation and justification are provided. Simulations under different scenarios provide comparison of the new control chart mechanism to those already established in the literature. Conclusions and recommendations for future research are discussed

Pattern recognition for manufacturing process variation using integrated statistical process control – artificial neural network

Variation in manufacturing process is known to be a major source of poor quality products and variation control is essential in quality improvement. In bivariate cases, which involve two correlated quality variables, the traditional statistical process control (SPC) charts are known to be effective in monitoring but they are lack of diagnosis. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Investigation has been focused on an integrated SPC - ANN model. This model utilizes the Exponentially Weighted Moving Average (EWMA) control chart and ANN model in two-stage monitoring and diagnosis technique. This scheme was validated in manufacturing of hard disc drive. The study focused on bivariate process for cross correlation function, ρ = 0.3 and 0.7 and mean shifts, μ = ±1.00-2.00 standard deviations. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart. In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variation. This scheme is effective for cases variations of such loading error, offsetting tool and inconsistent pressure. Therefore, this study should be useful in minimizing the cost of waste materials and has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC

Estimating change point in multivariate processes via simultaneous mean vector and covariance matrix

In many industrial processes, several quality characteristics are inevitably related. In this situation, the mean vector and covariance matrix must be simultaneously monitored and controlled to determine whether a multivariate process is in control. With the increase in the number of variables, the performance of control charts is significantly reduced, and the time delay between the actual time of change in the process and the warning time of the control chart increases, which is one of the main challenges when using multivariable control charts. Between the real-time and the change time (called the change-point - CP), especially during the simultaneous monitoring and controlling of the parameters, the mean vector, and the covariance matrix cause problems such as delay or stoppage of the production lines or services, as well as inconsistent production of products or services. To improve this, a new way of estimating the CP will help statistical process control (SPC) professionals identify the cause(s) of out-of-control (OC) conditions, thus providing better feedback for process improvement. This study presented a new method based on an artificial neural network (ANN), which first examined the OC conditions for a multivariate process using the multivariate exponentially weighted moving average (MEWMA) and multivariate exponentially weighted mean square (MEWMS) control charts. Then, the ANN-fitting method was used to diagnose the cause(s) of OC conditions using the machine learning (ML)-classifier and estimating the length of delay time. Finally, the change point (CP) was estimated by integrating all these methods. The performance of the new approach was validated by comparing it with the results from another study. It also validated the proposed method developed by evaluating the accuracy and precision of this research. As a conclusion, the MEWMS chart was the best for detecting the OC condition while the support vector machines (SVM) gaussian model best to diagnoses the cause(s) o f the OC condition. The model provided has estimated the change point on one sample with difference over 10,000 tested cases (simulated) with a probability of 99%, which is an accurate and reliable model for a practical approach

Indicators for measuring satisfaction towards design quality of buildings

Design quality is an important component in measuring satisfaction towards total product quality (TPQ) of buildings, the product of construction projects. Design Quality Indicator (DQI), developed by the Construction Industry Council (CIC) in the UK looking at three quality fields, i.e. functionality, build quality, and impact of building in measuring the quality of design embodied in the buildings through feedback and perceptions of all stakeholders involved in the production and use of buildings. Design quality is always a major concern in the Malaysian construction industry. With inspiration from this DQI, this study was carried out to identify indicators for measuring the satisfaction towards design quality of buildings and to evaluate the suitability of the indicators for application in the context of Malaysian construction industry. Through literature survey, 32 indicators of design quality were identified and grouped into the three design quality fields. A questionnaire survey was carried out among Malaysian construction professionals (architects, engineers, quantity surveyors, contractors and developers) to assess the identified design quality indicators in terms of their relevance and significance in the context of construction industry in Malaysia. The survey reveals that access, natural lighting, access and use, structure element, landscape, finishes, location, external environment, urban and social integration and noise are among the design quality indicators that were perceived as the most important to be looked at. In overall, all the indicators are relevant for adoption in the Malaysian construction industry to measure the satisfaction towards design quality of buildings

Single phase inverter system using proportional resonant current control

This paper presents the harmonic reduction performance of proportional resonant (PR) current controller in single phase inverter system connected to nonlinear load. In the study, proportional resonant current controller and low pass filter is discussed to eliminate low order harmonics injection in single phase inverter system. The potential of nonlinear load in producing harmonics is showed and identified by developing a nonlinear load model using a full bridge rectifier circuit. The modelling and simulation is done in MATLAB Simulink while harmonic spectrum results are obtained using Fast Fourier Transfor. End result show PR current controller capability to overcome the injection of current harmonic problems thus improved the overall total harmonic distortion (THD)

Quantitative infrared thermography resolved leakage current problem in cathodic protection system

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, res

An Examination of the Robustness to Non Normality of the EWMA Control Charts for the Dispersion

The EWMA control chart is used to detect small shifts in a process. It has been shown that, for certain values of the smoothing parameter, the EWMA chart for the mean is robust to non normality. In this article, we examine the case of non normality in the EWMA charts for the dispersion. It is shown that we can have an EWMA chart for dispersion robust to non normality when non normality is not extreme.Average run length, Control charts, Exponntially weighted moving average control chart, Median run length, Non normality, Statistical process control