On Some Non-Manufacturing Applications of Counted Data Cumulative Sum (CUSUM) Control Chart Schemes

Osanaiye, P. A.; Talabi, C. O.

doi:10.2307/2349058

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…In recent years, it has been successfully used in other areas. In Osanaiye and Talabi (1989), the algorithm is used to detect possible outbreaks of epidemics. Grigg, Farewell, and Spiegelhalter (2003) analyzed the 30-day mortality for patients after cardiac surgery.…”

Section: Past Usages Of the Cusum Algorithmmentioning

confidence: 99%

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Trilles

Belmonte

Schade

et al. 2016

International Journal of Digital Earth

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Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user. ARTICLE HISTORY

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Section: Past Usages Of the Cusum Algorithmmentioning

confidence: 99%

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Trilles

Belmonte

Schade

et al. 2016

International Journal of Digital Earth

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show abstract

“…Lucas [4] gave a detailed procedure for designing a counted data Cusum chart based on a poisson counts and implemented it on the occurrence of industrial accidents. Osanaiye and Talabi [7] considered a versatile dimension on the application of Cusum chart in a non-manufacturing sector and implemented it on the cases of diabetes patients. Adekeye [8] applied the concept of cusum control chart to monitor the crime rate in Nigeria.…”

Section: The Cumulative Sum (Cusum) Control Chartmentioning

confidence: 99%

Design of Cusum Scheme for Monitoring Road Accident Fatalities

Adekeye¹,

Aluko²

2012

OJS

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In recent years, road accident fatalities in Nigeria have continued to be on the increase. Thus, urgent attention is needed to reduce or eliminate road accidents fatalities. To achieve this goal, the cumulative sum (Cusum) control chart scheme was designed for monitoring the road accident fatalities using the recorded occurrence of road accident fatalities in a state in the western part of Nigeria. The designed Cusum detects the period of the years when the highest occurrence of road accident fatalities occurred. These periods were observed to be festive periods such as Christmas, Easter, Eid-elKabir, and Eid-el-Moluod. Therefore, the festive periods of the year should be used as benchmark by road managers as periods where more attention or precaution measure should be put in place on the roads to drastically reduce or eliminate high occurrence of road accident fatalities. The designed Cusum control chart can be adapted for other states in the country and also for the larger society for detecting the periods when the rate of death as a result of road accidents was prevalent.

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“…This chart, first proposed by Page, 1 takes into consideration all the previous samples information jointly with that of the current one. Several authors have studied its performance including, for example, Crosier, 2 Goldsmith and Whitfield, 3 Kemp, 4 Brook and Evans, 5 Woodall,6,7 Waldmann, 8 Osanaiye and Talabi, 9 Mahmoud et al, 10 Luceno and Puig-pey, 11 Tan and Pooi, 12 Dunbar 13 and Wu et al 14 The ordinary two-sided CUSUM (O-CUSUM) chart proposed by Page 1 uses two chart statistics to monitor positive and negative shifts. Crosier 2 introduced a simpler two-sided CUSUM chart that depends only on one statistic for detecting both types simultaneously.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Crosier's CUSUM Control Chart with Estimated Parameters

Hany

Mahmoud

2015

Qual. Reliab. Engng. Int.

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We evaluate the performance of the Crosier's cumulative sum (C‐CUSUM) control chart when the probability distribution parameters of the underlying quality characteristic are estimated from Phase I data. Because the average run length (ARL) under estimated parameters is a random variable, we study the estimation effect on the chart performance in terms of the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL). Previous evaluations of this control chart were conducted while assuming known process parameters. Using the Markov chain and simulation approaches, we evaluate the in‐control performance of the chart and provide some quantiles for its in‐control ARL distribution under estimated parameters. We also compare the performance of the C‐CUSUM chart to that of the ordinary CUSUM (O‐CUSUM) chart when the process parameters are unknown. Our results show that large number of Phase I samples are required to achieve a quite reasonable performance. Additionally, the performance of the C‐CUSUM chart is found to be superior to that of the O‐CUSUM chart. Finally, we recommend the use of a recently proposed bootstrap procedure in designing the C‐CUSUM chart to guarantee, at a certain probability, that the in‐control ARL will be of at least the desired value using the available amount of Phase I data. Copyright © 2015 John Wiley & Sons, Ltd.

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On Some Non-Manufacturing Applications of Counted Data Cumulative Sum (CUSUM) Control Chart Schemes

Cited by 9 publications

References 6 publications

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Design of Cusum Scheme for Monitoring Road Accident Fatalities

An Evaluation of the Crosier's CUSUM Control Chart with Estimated Parameters

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