A general framework for monitoring complex processes with both in-control and out-of-control information

Zhang, Chi; Tsung, Fugee; Zou, Changliang

doi:10.1016/j.cie.2015.03.007

Cited by 33 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It applies in CL = 53, UCL = 111, and LCL = 0 (see Figure 10c). The centerline and control limits for thex-control diagram were calculated according to Equations (19)- (21). The values are as follows CL = 744, UCL = 830 and LCL = 658 (see Figure 10d).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

et al. 2020

View full text Add to dashboard Cite

At present, ever higher demands are placed on the quality of products. The success of organizations in the global market depends mainly on measuring and evaluating their products quality. A set of measurable criteria usually determines product quality. There are many technological processes in the structure of a production organization that is statistically unstable. The norms of ISO class 9000 emphasize statistical process control, known as SPC (Statistical Process Control). They represent a methodology for eliminating the causes of instability of production or technological process. The paper deals with the application of control charts for the technological process of rock disintegration by rotary drilling. The measured values of the dynamic system drilling tool-rock in working mode are processed. The control charts are applied to the input (control) variables of the pressure force-F (N), revolutions-n (rpm), and the output measured variable of the vibration signal of the acceleration. The article constructs and presents the resulting important control charts for the technological process of rock disintegration by rotary drilling. It is essential that for the technological process of rock drilling, the variables that enter and exit the dynamic system must be statistically manageable. The stable state of the input technical parameters (revolutions and pressure force) of the drilling tool is essential from the technological, performance, and economic point of view. The stable state of the output parameters is of significant importance in preventing the emergency state, excessive wear of the drilling equipment and optimizing the optimal operating mode. Industrial practice points out that the correct application of statistical regulation stabilizes the technological process, increasing the quality and productivity of work.

show abstract

Section: Resultsmentioning

confidence: 99%

“…A control chart is then a graphical tool showing the subgroup's given selection characteristic to its sequence number. According to the international standard STN ISO 8258 and depending on the nature of the monitored quality feature, the classic control charts are divided into [19][20][21][22][23]:…”

Section: Introductionmentioning

confidence: 99%

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

et al. 2020

View full text Add to dashboard Cite

show abstract

“…While the ARL 1 is the likely number of samples before an out‐of‐control signal when the process is indeed, out‐of‐control. A control chart with smaller ARL 1 values at more points is considered to be more effective than other charts (Zhang et al).…”

Section: Simulation Results and Performance Measuresmentioning

confidence: 99%

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

Zaman

Lee

Riaz

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

Random causes are vital part of every process in manufacturing and nonmanufacturing environments, and these do not affect the product features. Special causes, on the other hand, come because of some burden(s) in a process and requires special attention; otherwise, it ruins the products excellence. Special causes are categorized into small, moderate, and large shifts and are handled by statistical quality control charts. The Shewhart control chart is well known for large shifts, while the cumulative sum and exponentially weighted moving average are more effective in detecting small to moderate shifts. However, in practice, many processes require the simultaneous monitoring of both the small to the large shifts. In this study, we have designed an adaptive EWMA for dispersion parameter in connection with Huber and Tukey's bisquare functions. The performance measures used in this study include average run length, extra quadratic loss, relative average run length, and performancecomparison index. We have observed that the study proposals are good competitors to the other counter parts for an efficient monitoring of shifts of varying amounts. An illustrative example using real data is given to demonstrate the implementation of the study proposal.

show abstract

“…Xanthopoulos and Razzaghi used the weighted SVM approach for imbalanced pattern recognition. Zhang et al developed a general monitoring framework for detecting location shifts in complex processes using SVM model and multivariate EWMA chart. Zhou et al investigated a method of control chart pattern recognition by integrating fuzzy SVMs with hybrid kernel function and genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

One‐sided control chart based on support vector machines with differential evolution algorithm

Wang

Bizuneh

Cheng

2019

Quality & Reliability Eng

View full text Add to dashboard Cite

The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one-sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non-normal distribution cases. The ARL performance of the proposed chart is better than the distance-based SVM chart. A real example is used to illustrate the application of the proposed control chart.

show abstract

A general framework for monitoring complex processes with both in-control and out-of-control information

Cited by 33 publications

References 33 publications

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

One‐sided control chart based on support vector machines with differential evolution algorithm

Contact Info

Product

Resources

About