Evaluation of Linear Trend Tests Using Resampling Techniques

Vafeiadis, Thanasis; Bora-Senta, E.; Kugiumtzis, Dimitris

doi:10.1080/03610910701858371

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…The SSP method estimates the change point (or the breakpoint T) from the profile of a linear trend test statistic, computed on consecutive overlapping time windows along the time series. The selected test statistic for linear trend estimation it is showed that gives high power compared to other test statistics for both correlated and white noise residuals [19]. In SSP approach, a first candidate breakpoint is the time point at which the profile crosses the threshold line of rejection of the null hypothesis of no trend at ± and segment (− as and , respectively.…”

Section: A Slope Statistic Profile (Ssp) Methodsmentioning

confidence: 99%

Real-time incident detection: An approach for two interdependent time series

Vafeiadis¹,

Ioannidis²,

Krinidis³

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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A method is proposed to detect incidents that occur in two interdependent time series in real-time, estimating the incident time point from the profiles of the linear trend test statistics, computed on consecutive overlapping data window. The method is based on Slope Statistics Profile (SSP) utilizing adaptive data windowing, estimating real-time classifications of the linear trend profiles, according to two different linear trend scenarios, suitably adapted to the conditions of the problem. The method is applied on real datasets from a chemical process system that is situated at the premises of CERTH / CPERI, suggesting the occurrence of incidents, during experiments.

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Section: A Slope Statistic Profile (Ssp) Methodsmentioning

confidence: 99%

Real-time incident detection: An approach for two interdependent time series

Vafeiadis¹,

Ioannidis²,

Krinidis³

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Early malfunction diagnosis of industrial process units utilizing online linear trend profiles and real‐time classification

Vafeiadis

Ziogou

Stavropoulos

et al. 2018

Adaptive Control & Signal

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SummaryThe early detection of potential malfunctions at process systems can significantly reduce downtime and improve their overall operability. In that context, this paper demonstrates the behavior and response, through a comparative analysis, of novel data‐driven diagnosis methods for interdependent time series. The proposed real‐time slope statistic profile method utilizes a self‐adaptive sliding window based on a real‐time classification technique of linear trend profiles of both interdependent time series and internal condition so as to avoid misdetections. The calculation of the linear trend profile is based on a standard parametric linear trend test, and the selection of possible incidents is based on its two‐level cross‐checking. All possible combinations for the calculation of the trend test and cross‐checking are created to explore their efficiency. The proposed methods are tested against real data sets from a chemical process system of the Centre for Research and Technology Hellas/Chemical Process Energy and Resources Institute derived from specific scenarios during nominal operating conditions.

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