Methodology for Application of Non-Parametric Methods of Statistical Process Control Into Practice

Smajdorová, Tereza; Noskievičová, Darja

doi:10.7441/dokbat.2019.092

Cited by 1 publication

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the lower part of the sheet, the resulting NPCC is constructed. All computations and diagrams are made automatically after inserting the input values [51].…”

Section: Nonparametric Control Chart Sw Supportmentioning

confidence: 99%

“…Regarding ARL(0) and MRL(0), the latter is the more stable indicator. Accordingly, the following performance evaluation of analyzed NPCCs for statistically stable processes was made using only x 5 and MRL(0) performance indicators [51]. As an example of this analysis, there are plots of performance indicators in relation to various violations of data assumptions represented by seven different probability distributions (see Table 1) for the SSCC control that can be found in Appendix A.…”

Section: Selection Of Control Chart Performance Indicatorsmentioning

confidence: 99%

“…Such plots were constructed for all the rest of the analyzed NPCCs. Based on a similar analysis of statistically unstable processes, only x 95 and MRL(δ) were utilized for evaluation of NPCC performance [51].…”

Section: Selection Of Control Chart Performance Indicatorsmentioning

confidence: 99%

“…This can be explained by the fact that NPCCs perceive isolated change as a random deviation, against which they are robust [51].…”

Section: Summary Of Simulations Of Statistically Unstable Processmentioning

confidence: 99%

See 3 more Smart Citations

Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes

Smajdorová

Noskievičová

2022

Applied Sciences

View full text Add to dashboard Cite

Nonparametric control charts (NPCC) have shown great potential for monitoring processes in conditions of smart manufacturing with complex structures, various monitored characteristics and the need to process big data. Practical applications of NPCCs are very rare. The main reasons for this situation are a deficiency in software support and a lack of simple but complete instructions for their application. The introduction of such manual, which is based on the authors’ own simulations of performance of wide spectrum of NPCCs in conditions of different violations of data prerequisites, leading to recommendations for the selection of the most effective NPCC in various practical situations, is the main goal of this paper. Compared to other similar studies, this approach covers a wider range of control charts, and it was applied to a wider spectrum of data assumption violations. As an integral part of these analyses, an examination of various control chart performance indicators such as ARL, MRL, x5 and x95 was performed using simulations to select the best of them. The designed methodology was verified using real data.

show abstract

“…In the lower part of the sheet, the resulting NPCC is constructed. All computations and diagrams are made automatically after inserting the input values [51].…”

Section: Nonparametric Control Chart Sw Supportmentioning

confidence: 99%

Section: Selection Of Control Chart Performance Indicatorsmentioning

confidence: 99%

Section: Selection Of Control Chart Performance Indicatorsmentioning

confidence: 99%

“…This can be explained by the fact that NPCCs perceive isolated change as a random deviation, against which they are robust [51].…”

Section: Summary Of Simulations Of Statistically Unstable Processmentioning

confidence: 99%

See 2 more Smart Citations