scholarly journals Mixed Moving Average-Cumulative Sum Control Chart for Monitoring Parameter Change

2022 ◽  
Vol 31 (1) ◽  
pp. 635-647
Author(s):  
Nongnuch Saengsura ◽  
Saowanit Sukparungsee ◽  
Yupaporn Areepong
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Parameter Change ◽  
Cumulative Sum ◽  
Monitoring Parameter ◽  
Cumulative Sum Control

Multivariate cumulative sum control chart and measure of process capability based on bivariate ranked set schemes

2020 ◽  
Vol 150 ◽  
pp. 106891
Author(s):  
Rashid Mehmood ◽  
Muhammad Hisyam Lee ◽  
Iftikhar Ali ◽  
Muhammad Riaz ◽  
Shahid Hussain
Keyword(s):  
Control Chart ◽  
Process Capability ◽  
Cumulative Sum ◽  
Cumulative Sum Control

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model

2017 ◽  
Vol 27 (9) ◽  
pp. 2859-2871 ◽  
Author(s):  
Orlando Yesid Esparza Albarracin ◽  
Airlane Pereira Alencar ◽  
Linda Lee Ho
Keyword(s):  
Control Charts ◽  
Negative Binomial ◽  
Moving Average ◽  
Health Surveillance ◽  
Autoregressive Moving Average ◽  
Cumulative Sum ◽  
Moving Average Models ◽  
Cumulative Sum Control ◽  
The Impact ◽  
Autoregressive Moving Average Models

Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion. To include serial correlations, generalized autoregressive moving average models are proposed. The main contribution of the current article is to measure the impact, in terms of average run length on the performance of cumulative sum charts when the serial correlation is neglected in the regression model. Different statistics based on transformations, the deviance residual, and the likelihood ratio are used to build cumulative sum control charts to monitor counts with time varying means, including trend and seasonal effects. The monitoring of the weekly number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current method.

scholarly journals Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart

2021 ◽  
Vol 49 (3) ◽  
pp. 684-695
Author(s):  
Jawad Mohammed ◽  
Jaber Abdulhady
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Time Domain ◽  
Signal Analysis ◽  
Detection System ◽  
Significant Feature ◽  
Test Rig ◽  
Cumulative Sum ◽  
Analysis Technique ◽  
Cumulative Sum Control

Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.

Cumulative sum control chart (CUSUM)

2021 ◽  
pp. 559-568
Author(s):  
Bahman Zohuri ◽  
Patrick McDaniel
Keyword(s):  
Control Chart ◽  
Cumulative Sum ◽  
Cumulative Sum Control

A mixed cumulative sum homogeneously weighted moving average control chart for monitoring process mean

2020 ◽  
Author(s):  
Muhammad Abid ◽  
Sun Mei ◽  
Hafiz Zafar Nazir ◽  
Muhammad Riaz ◽  
Shahid Hussain ◽  
...  
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Process Mean ◽  
Cumulative Sum ◽  
Monitoring Process ◽  
Average Control ◽  
Weighted Moving Average
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