Monitoring bivariate and trivariate mean vectors with a Shewhart chart

2017 ◽  
Vol 33 (8) ◽  
pp. 2035-2042 ◽  
Author(s):  
Roberto Campos Leoni ◽  
Antonio Fernando Branco Costa
Keyword(s):  
Shewhart Chart ◽  
Mean Vectors

Detection of Shifts in GPS Measurements for a Long-Span Bridge Using CUSUM Chart

2016 ◽  
Vol 16 (04) ◽  
pp. 1640024 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Gangbing Song ◽  
Qing Guo
Keyword(s):  
Deformation Monitoring ◽  
Experimental Results ◽  
High Rate ◽  
Cumulative Distribution ◽  
Control Technique ◽  
Cusum Chart ◽  
Shewhart Chart ◽  
Long Span ◽  
Long Span Bridge ◽  
Shift Detection

Timely and correctly evaluating the quality of Global Positioning System (GPS) data is essential for reduction in the number of false alarms and missed detection of a GPS-based bridge deformation monitoring system. This paper investigates how to use the statistical process control technique, known as the cumulative sum (CUSUM) chart, for the detection of small but persistent shifts in the high-rate GPS carrier-phase measurements. First, a mathematical model for the shift detection based on the continuous hypothesis testing is established. The main features and implementation procedure of the CUSUM chart for the shift detection are then summarized, and the corresponding parameter selection method is discussed in detail. To meet the normality requirement of the CUSUM chart, a novel method that transfers the data to the Q-statistic by the estimated cumulative distribution functions is proposed according to the probability integral transform theory. This is followed by a simulation carried out to evaluate the detection performance of the CUSUM chart and exploit its advantages to the commonly used Shewhart chart for the high-rate GPS monitoring data with different shift sizes. Experimental results have showed that the CUSUM chart is sensitive to small persistent shifts compared to the Shewhart chart although it has a delay problem. The integration of CUSUM chart and Shewhart chart would be a reliable approach for the shift detection. Finally, an on-site dynamic monitoring experiment is carried out on a long-span bridge to validate the proposed approach’s effectiveness in detecting an actual deformation shift, and the experimental results proved to be very encouraging.

scholarly journals Analyzing risks of occupational injuries in basic industries

2021 ◽  
pp. 119-126
Author(s):  
V.A. Pankov ◽  
◽  
M.V. Kuleshova ◽  
Keyword(s):  
Linear Regression ◽  
Safety Management ◽  
Occupational Injuries ◽  
Health And Safety ◽  
Wood Processing ◽  
Shewhart Chart ◽  
Upper Limit ◽  
Irkutsk Region ◽  
Using Data ◽  
Aircraft Production

Our research aim was to analyze occupational injuries in basic industries in Irkutsk region. Materials and methods. Occupational injuries (OI) in basic industries were analyzed using data from statistical reports issued in 2010–2019. To analyze OI in dynamics, we calculated relative values of OI and applied linear regression and Shewhart charts. Normalized intensity indicators method was used to reveal different probability of injuries in various industries as well as to predict OI risks. Results. Analysis of OI in dynamics indicates that there is a stable descending trend in a number of injuries. However, in spite of this apparent descending trend, OI values are stably by 1.3–3.0 times higher in some industries than on average in the region. The highest frequency coefficient (FC) for occupational injuries was detected in wood processing where it was equal to 5.35 [2.90–7.71] per 1,000 workers; the indicator varied within 1.00–2.93 per 1,000 workers in other industries. Shewhart chart for FC indicates that systems of occupational health and safety management are not efficient enough in all the analyzed industries since FC exceeds the upper limit in some years. We established that severity of occupational injuries tended to grow in wood processing (Cs = +3.23; 5.33 %), metallurgy (Cs = +0.94; 1.26 %), land transport (Cs = +2.42; 4.39 %), and aircraft production (Cs = +0.59; 1.68 %). The greatest number of fatal OI was detected in mining, construction, and agriculture as a share of fatal OI in the overall structure of occupational injuries amounted to 22.0 %, 19.2 %, and 11.7 % in these brunches accordingly. A probability that an injury becomes fatal is also the highest in them, 11.7, 9.0, and 6.0 accordingly. “Wood processing and production of wood articles”, “Aircraft production”, and “Construction” are among industries where risks of occupational injuries are the most probable.

scholarly journals Fever in Children - an Assessment of Validity by a Shewhart Model in a Syndromic Surveillance System in China

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Changming Zhou ◽  
Huijian Cheng ◽  
Genming Zhao ◽  
Qi Zhao ◽  
Biao Xu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Case Report ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Disease Outbreaks ◽  
Syndromic Surveillance System ◽  
Infectious Disease Outbreaks ◽  
Shewhart Chart ◽  
Common Infectious Disease ◽  
Report System

The objective is to evaluate the validity of the signals generated by Shewhart chart to detect the increase in febrile children with patients with common infectious diseases. There were 28,049 and 42,029 reports for febrile patients in the two study counties during the 2-year period. The sensitivity were 29.03% and 34.78%. The PPVs were 64.29% and 53.33%. The sensitivity of signals in the syndromic surveillance system was low using the Shewhart model while the PPV was relatively high which suggested that this syndromic surveillance system had potential ability to supplement conventional case report system in detecting common infectious disease outbreaks.

A multi-rule Shewhart chart for quality control in clinical chemistry.

1981 ◽  
Vol 27 (3) ◽  
pp. 493-501 ◽  
Author(s):  
J O Westgard ◽  
P L Barry ◽  
M R Hunt ◽  
T Groth
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Shewhart Chart

Performance of Shewhart Chart and Multivariate Hotelling T-Square Control Chart in Textile Industry

2018 ◽  
Vol 9 (11) ◽  
pp. 1737-1745
Author(s):  
S. Subbulakshmi ◽  
A. Kachimohideen ◽  
R. Sasikumar
Keyword(s):  
Control Chart ◽  
Textile Industry ◽  
Shewhart Chart
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