A CUSUM-based method for monitoring simple linear profiles

Saghaei, Abbas; Mehrjoo, Marzieh; Amiri, Amirhossein

doi:10.1007/s00170-009-2063-2

Cited by 106 publications

(50 citation statements)

References 18 publications

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“…The null hypothesis tested by both CUSUM and MOSUM is that regression coefficients of a linear model are constant over time; the alternative hypothesis is that the coefficients change over time due to influence of an external factor. Both methods have been used for change detection in eco-hydrology (de Jong et al, 2012;Verbesselt et al, 2010;Vogl and Lopes, 2010;Webb et al, 2012;de Jong et al, 2013;Verbesselt et al, 2012;Tsutsumida et al, 2013;Forkel et al, 2013), economic analysis (Caporale et al, 2011;Ghosh, 2009;Olmo et al, 2011;Tiwari et al, 2012) and quality control (Saghaei et al, 2009). However, CUSUM is considered to be less sensitive to certain changes in regression coefficients especially if the changes occur in the later dates of the period under consideration (Bauer and Hackl, 1978;Chu et al, 1995).…”

Section: Structural Changes In Monthly Rainfall-streamflow Relationshipsmentioning

confidence: 99%

Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

Jayakaran

Williams

Ssegane

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Hurricanes are infrequent but influential disruptors of ecosystem processes in the southeastern Atlantic and Gulf coasts. Every southeastern forested wetland has the potential to be struck by a tropical cyclone. We examined the impact of Hurricane Hugo on two paired coastal South Carolina watersheds in terms of streamflow and vegetation dynamics, both before and after the hurricane's passage in 1989. The study objectives were to quantify the magnitude and timing of changes including a reversal in relative streamflow difference between two paired watersheds, and to examine the selective impacts of a hurricane on the vegetative composition of the forest. We related these impacts to their potential contribution to change watershed hydrology through altered evapotranspiration processes. Using over 30 years of monthly rainfall and streamflow data we showed that there was a significant transformation in the hydrologic character of the two watersheds -a transformation that occurred soon after the hurricane's passage. We linked the change in the rainfall-runoff relationship to a catastrophic change in forest vegetation due to selective hurricane damage. While both watersheds were located in the path of the hurricane, extant forest structure varied between the two watersheds as a function of experimental forest management techniques on the treatment watershed. We showed that the primary damage was to older pines, and to some extent larger hardwood trees. We believe that lowered vegetative water use impacted both watersheds with increased outflows on both watersheds due to loss of trees following hurricane impact. However, one watershed was able to recover to pre hurricane levels of evapotranspiration at a quicker rate due to the greater abundance of pine seedlings and saplings in that watershed.

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Section: Structural Changes In Monthly Rainfall-streamflow Relationshipsmentioning

confidence: 99%

Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

Jayakaran

Williams

Ssegane

et al. 2014

Hydrol. Earth Syst. Sci.

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“…Noorossana et al (2008) investigated the effect of autocorrelation between linear profiles on the performance of T 2 control chart proposed by Kang and Albin (2000). Saghaei et al (2009) proposed a method based on cumulative sum statistics for monitoring of linear profiles in phase II. Zhang and Wang (2009) constructed control chart based on likelihood ratio for monitoring linear profiles in phase II.…”

Section: Literature Review On Monitoring the Quality Profiles In Phase Imentioning

confidence: 99%

Developing a univariate approach to phase-I monitoring of fuzzy quality profiles

Noghondarian¹,

Ghobadi²

2012

IJIEC

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In many real-world applications, the quality of a process or a particular product can be characterized by a functional relationship called profile. A profile builds the relationships between a response quality characteristic and one or more explanatory variables. Monitoring the quality of a profile is implemented to understand and to verify the stability of this functional relationship over time. In some real applications, a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is fuzzy. The purpose of this paper is to develop an approach for monitoring process/product profiles in fuzzy environment. A model in fuzzy linear regression is developed to construct the quality profiles by using linear programming and then fuzzy individuals and moving-range (I-MR) control charts are developed to monitor both intercept and slope of fuzzy profiles to achieve an in-control process. A case study in customer satisfaction is presented to show the application of our approach and to express the sensitivity analysis of parameters for building a fuzzy profile.

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“…Mestek et al (1994), Stover and Brill (1998), Kang and Albin (2000), Kim et al (2003), and Mahmoud and Woodall (2004) are just a few researchers who worked on profile monitoring. In addition, Niaki et al (2007), Saghaei et al (2009), Zhang et al (2009, Zhu and Lin (2010), Noorossana et al (2010), Amiri et al (2011) and Narvand et al (2013) are some researchers who worked on Phase-II profile monitoring. Besides, Du et al (2015a, b) investigated the cascade effects in multistage processes.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Phase-I monitoring of standard deviations in multistage linear profiles

et al. 2017

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In most modern manufacturing systems, products are often the output of some multistage processes. In these processes, the stages are dependent on each other, where the output quality of each stage depends also on the output quality of the previous stages. This property is called the cascade property. Although there are many studies in multistage process monitoring, there are fewer works on profile monitoring in multistage processes, especially on the variability monitoring of a multistage profile in Phase-I for which no research is found in the literature. In this paper, a new methodology is proposed to monitor the standard deviation involved in a simple linear profile designed in Phase I to monitor multistage processes with the cascade property. To this aim, an autoregressive correlation model between the stages is considered first. Then, the effect of the cascade property on the performances of three types of T 2 control charts in Phase I with shifts in standard deviation is investigated. As we show that this effect is significant, a U statistic is next used to remove the cascade effect, based on which the investigated control charts are modified. Simulation studies reveal good performances of the modified control charts.

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A CUSUM-based method for monitoring simple linear profiles

Cited by 106 publications

References 18 publications

Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

Hurricane impacts on a pair of coastal forested watersheds: implications of selective hurricane damage to forest structure and streamflow dynamics

Developing a univariate approach to phase-I monitoring of fuzzy quality profiles

Phase-I monitoring of standard deviations in multistage linear profiles

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