scholarly journals Control Limits for an Adaptive Self-Starting Distribution-Free CUSUM Based on Sequential Ranks

Technologies ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 71
Author(s):  
Michael Lang
Keyword(s):  
Control Charts ◽  
Simulation Experiments ◽  
Statistical Process ◽  
Distribution Free ◽  
Extensive Simulation ◽  
Detection Delay ◽  
Calibration Algorithm ◽  
Cusum Control Charts ◽  
Control Limits ◽  
Large Representative

Since their introduction in 1954, cumulative sum (CUSUM) control charts have seen a widespread use beyond the conventional realm of statistical process control (SPC). While off-the-shelf implementations aimed at practitioners are available, their successful use is often hampered by inherent limitations which make them not easily reconcilable with real-world scenarios. Challenges commonly arise regarding a lack of robustness due to underlying parametric assumptions or requiring the availability of large representative training datasets. We evaluate an adaptive distribution-free CUSUM based on sequential ranks which is self-starting and provide detailed pseudo-code of a simple, yet effective calibration algorithm. The main contribution of this paper is in providing a set of ready-to-use tables of control limits suitable to a wide variety of applications where a departure from the underlying sampling distribution to a stochastically larger distribution is of interest. Performance of the proposed tabularized control limits is assessed and compared to competing approaches through extensive simulation experiments. The proposed control limits are shown to yield significantly increased agility (reduced detection delay) while maintaining good overall robustness.

STATISTICAL PROCESS CONTROL FOR LOW NONCONFORMITY PROCESSES

1995 ◽  
Vol 02 (01) ◽  
pp. 15-22 ◽  
Author(s):  
T.N. GOH ◽  
M. XIE
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Exact Probability ◽  
Statistical Process ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Cumulative Count ◽  
Pattern Recognition Approach ◽  
Control Limits

Statistical process control of high quality products is an important issue in modern quality control applications because of the success of continuous improvement efforts worldwide. The conventional Shewhart control charts based on 3-sigma control limits tend to encounter certain practical and theoretical problems as “zero-defect” is approached. In this paper, we describe some general approaches to solving this problem, focusing on the control charts for nonconformities or defects. We suggest that for a moderate nonconformity process, the exact probability limits should be used. For a lower non-conformity process, a “pattern recognition” approach can be applied. Finally, for a near-zero nonconformity process, a modified approach based on the cumulative count of nonconformities can be used.

scholarly journals Construction of Fuzzy Control Chart with Multinomial Quality using Process Capability

2019 ◽  
Vol 9 (1) ◽  
pp. 1460-1465
Keyword(s):  
Fuzzy Control ◽  
Control Charts ◽  
Multinomial Distribution ◽  
Process Capability ◽  
Fuzzy Sets Theory ◽  
Statistical Process ◽  
Quantitative Form ◽  
The Times ◽  
Sets Theory ◽  
Control Limits

Control charts are the effective and quietest form of statistical process control methods. Many of the times, data are obtained in quantitative form; however there are many quality characteristics that cannot be expressed in numerical measure, such as characteristics for appearance, smoothness and colour, etc. Fuzzy sets theory is an impressive mathematical methodology to evaluate the vagueness related uncertainty that can linguistically express data in these situations. In this paper, we construct a fuzzy control limits under fixed and varying sample size with various quality levels for observing a manufacturing process based on the multinomial distribution using degrees of membership and process capability.

scholarly journals Construction of Fuzzy Mean using Standard Deviation ( X  S  ) Control Chart with Process Capability

2019 ◽  
Vol 8 (4) ◽  
pp. 5390-5396
Keyword(s):  
Quality Control ◽  
Standard Deviation ◽  
Set Theory ◽  
Control Chart ◽  
Control Charts ◽  
Total Quality ◽  
Statistical Process ◽  
Research Article ◽  
Fuzzy Mean ◽  
Control Limits

The Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capabilityThe Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capability

Monitoring Digital Technologies in Hydraulic Systems Using CUSUM Control Charts

2019 ◽  
Author(s):  
Farid Breidi ◽  
Abdallah Chehade ◽  
John Lumkes
Keyword(s):  
Time Series ◽  
Real Time ◽  
Condition Monitoring ◽  
Control Charts ◽  
High Speed ◽  
Fluid Power ◽  
Time Shift ◽  
Statistical Process ◽  
Pressure Time ◽  
Cusum Control Charts

Abstract Digital fluid power is a growing field which utilizes electronics and advanced controls to improve efficiencies, energy savings, and productivity in fluid power systems. Often relying on on/off high-speed switching techniques, digital hydraulics relies heavily on the performance of valves, where an error in the valve performance could lead to a major drop in the efficiency and performance of the entire system. Specifically, digital pump/motors are sensitive to valve delay and transition timing which negatively impacts their performance and condition with time. One approach to assessing the performance and efficiency of digital pump/motors is via monitoring its inlet (low) and outlet (high) pressure time-series. Real-time condition monitoring also supports preventive maintenance and provides a better understanding of the dynamics of pump/motors. For condition monitoring, Statistical Process Control (SPC) charts are often designed to detect shift changes in time-series. This paper proposes to construct two cumulative sum (CUSUM) control charts for fast real-time shift detection in the high and low pressure time-series of digital pump/motors. The proposed method will be able to actively detect common misbehaviors in the valves utilized in the digital pump/motor. The model have been successfully tested on a three-piston inline digital pump/motor, but this monitoring technique can be modified and implemented on other digital technology classes where valve performance is key in the success of the system.

scholarly journals Time Control Chart for Two Inverted Models

2018 ◽  
Vol 7 (3.31) ◽  
pp. 133
Author(s):  
R Subba Rao ◽  
M Pushpa Latha ◽  
R R.L. Kantam
Keyword(s):  
Control Charts ◽  
Life Time ◽  
Quality Characteristic ◽  
Time To Failure ◽  
Time Data ◽  
Statistical Process ◽  
Failure Times ◽  
Process Adjustment ◽  
Time Control ◽  
Control Limits

Control charts are one of the powerful techniques of Statistical Process Control. Control charts are widely accepted and applied in industry which can be used to improve productivity, prevent defects and unnecessary process adjustment. Moreover, they also provide information in diagnosis and process capability. Life time data generally contain the failure times of sample products or inter failure times or number of failures experienced in a given time.  The time to failure of a product is to be considered as a quality characteristic to assess the quality of the product.  Control limits are evaluated for the time to failure.  In this paper the time to failure of a product is considered to follow Inverse Rayleigh and Inverse Half Logistic distributions.  Life time data are compared with the control limits to judge the quality performance of the product.  

Robust skewness dispersion control charts

2018 ◽  
Vol 35 (10) ◽  
pp. 2136-2156
Author(s):  
Muhammad Rizwan Iqbal ◽  
Sajdah Hassan
Keyword(s):  
Control Charts ◽  
Design Methodology ◽  
Real Data ◽  
Distribution Free ◽  
Content Type ◽  
Normal Distributions ◽  
Type Process ◽  
Free Environment ◽  
Probability To Signal ◽  
Control Limits

Purpose The purpose of this paper is to explore the scope of robust dispersion control charts in a distribution-free environment, which is a specific case of non-normal control charts. These control charts are skewness-based structures designed to monitor skewed-type processes whilst equally performing under symmetric processes. Moreover, the choice of a suitable control chart for a particular non-normal situation is also suggested. Design/methodology/approach The probability control limits approach is considered as an alternative way to determine the skewness-based structure of dispersion control charts. The proposals of five robust and two conventional Shewhart-type dispersion control charts are suggested as efficient competitors of skewness correction (SC) dispersion control charts. The evaluation of robust proposals and competing dispersion control charts is done through false alarm rate (FAR) and probability to signal (PTS) measures. Findings The proposed dispersion control charts are found robust and efficient alternatives of SC dispersion control charts in both normal and non-normal distributions. The FARs and PTS properties of proposed control charts are impressive in all studied cases, and a real-data example also verifies the dominance of proposed control charts. Originality/value Conventional dispersion control charts quickly lose their efficiency as underlying process distribution deviates from normality; however, robust control charts emerge as most suitable candidates in such situations. This paper proposes the idea of robust dispersion control charts under a distribution-free structure for the skewed-type process, which is not yet explored.

scholarly journals The Modified Mixed Exponentially Weighted Moving Average-Cumulative Sum Control Charts for Autocorrelated Process

2021 ◽  
Author(s):  
Dushyant Tyagi ◽  
Vipin Yadav
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Statistical Process ◽  
Autocorrelated Process ◽  
Independent Process ◽  
Cumulative Sum Control ◽  
Process Observation ◽  
Control Limits

Statistical Process Control (SPC) is an efficient methodology for monitoring, managing, analysing and recuperating process performance. Implementation of SPC in industries results in biggest benefits, as enhanced quality products and reduced process variation. While dealing with the theory of control chart we generally move with the assumption of independent process observation. But in practice usually, for most of the processes the observations are autocorrelated which degrades the ability of control chart application. The loss caused by autocorrelation can be obliterated by making modifications in the traditional control charts. The article presented here refers to a combination of EWMA and CUSUM charting techniques supplementing modifications in the control limits. The performance of the referred scheme is measured by comparing average run length (ARL) with existing control charts. Also, the referred scheme is found reasonably well for detecting particularly smaller displacements in the process.

scholarly journals Distribution-Free CUSUM-Type Control Charts for Monitoring Industrial Processes: An Overview

2021 ◽  
Vol 6 (4) ◽  
pp. 975-1008
Author(s):  
Ioannis S. Triantafyllou ◽  
Mangey Ram
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Crucial Role ◽  
Control Charts ◽  
Industrial Processes ◽  
Cumulative Sum ◽  
Statistical Process ◽  
Distribution Free ◽  
Practical Applications ◽  
Memory Type

In the present paper we provide an up-to-date overview of nonparametric Cumulative Sum (CUSUM) monitoring schemes. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output does not match to a particular distributional model. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric CUSUM-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online Statistical Process Control.

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