A review of alarm root cause analysis in process industries: Common methods, recent research status and challenges

Alinezhad, Haniyeh Seyed; Roohi, Mohammad Hossein; Chen, Tongwen

doi:10.1016/j.cherd.2022.10.041

Cited by 20 publications

(8 citation statements)

References 79 publications

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“…In any case, the differences detected when the standard deviation correction is performed would produce effects analogous or slightly underestimated to the uncorrected effect measurements. Equation (7) has more power to measure the effect because it has more type 1 errors. Therefore, the measured effects of the fullmatch models, using Equation (7), provide sufficient reliability to be considered.…”

Section: Advantages and Disadvantages Of This Methodologymentioning

confidence: 99%

“…Equation (7) has more power to measure the effect because it has more type 1 errors. Therefore, the measured effects of the fullmatch models, using Equation (7), provide sufficient reliability to be considered.…”

Section: Advantages and Disadvantages Of This Methodologymentioning

confidence: 99%

“…Almizahad et al synthesized the different methods of root cause analysis for both time series and cross-sectional data [7]. For time series, two approaches are available depending on whether they use multivariate Granger causality (MGC) [12] or transfer entropy (TE) methods.…”

Section: Causality Techniques Applied To the Manufacturing Processmentioning

confidence: 99%

“…MGC extends bivariate Granger causality to multiple variables and its advantage is that it has a low computational cost, is easier to implement, and works well with little data [16]. Principal component analysis is traditionally used as a feature selection method [7]. The main applications of MGC in root cause analysis are indicated in Table A1.…”

Section: Causality Techniques Applied To the Manufacturing Processmentioning

confidence: 99%

“…Causal research in the manufacturing industry has been concentrated on the alarm root cause analysis, presenting multiple data-driven methods, such as those oriented to timeseries data (Granger causality or entropy transfer), as well as methods oriented towards cross-sectional data, such as machine learning and probabilistic graphical models [7]. However, the former cannot be applied because the actual available data for downtimes only records the date but not the time when it occurs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry

Aviles-Lopez,

Luna del Castillo,

Montero-Alonso

2023

Mathematics

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This paper aims to present a methodology for the application of matching methods in industry to measure causal effect size. Matching methods allow us to obtain treatment and control samples with their covariates as similar as possible. The matching techniques used are nearest, optimal, full, coarsened exact matching (CEM), and genetic. These methods have been widely used in medical, psychological, and economic sciences. The proposed methodology provides two algorithms to execute these methods and to conduct an exhaustive search for the best models. It uses three conditions to ensure, as far as possible, the balance of all covariates, the maximum number of units in the treatment and control groups, and the most significant causal effect sizes. These techniques are applied in the carton board industry, where the causal variable is downtime, and the outcome variable is waste generated. A dataset from the carton board industry is used, and the results are contrasted with an expert in this process. Meta-analysis techniques are used to integrate the results of different comparative studies, which could help to determine and prioritize where to reduce waste. Two machines were found to generate more waste in terms of standardized measures whose values are 0.52 and 0.53, representing 48.60 and 36.79 linear meters (LM) on average for each production order with a total downtime of more than 3000 s. In general, for all machines, the maximum average wastage for each production order is 24.98 LM and its confidence interval is [13.40;36.23] LM. The main contribution of this work is the use of causal methodology to estimate the effect of downtime on waste in an industry. Particularly relevant is the contribution of an algorithm that aims to obtain the best matching model for this application. Its advantages and disadvantages are evaluated, and future areas of research are outlined. We believe that this methodology can be applied to other industries and fields of knowledge.

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Section: Advantages and Disadvantages Of This Methodologymentioning

confidence: 99%

Section: Advantages and Disadvantages Of This Methodologymentioning

confidence: 99%