2017
DOI: 10.4172/2157-7048.1000328
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Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Abstract: Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored variables, process monitoring techniques can be classified into univariate or multivariate techniques. Most univariate pr… Show more

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Cited by 4 publications
(5 citation statements)
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“…This approach attempts to preserve the main trends of the data while reducing unnecessary stochastic features. This is similar to the approach demonstrated in [ 5 ], which does not model the approximate signal in its algorithm. Upon examining the detection performance of both techniques, results show that retaining the approximate signal is better for detecting faults.…”
Section: New Coefficient Selection Criterion and Enhanced Mspca (Emsp...supporting
confidence: 59%
See 3 more Smart Citations
“…This approach attempts to preserve the main trends of the data while reducing unnecessary stochastic features. This is similar to the approach demonstrated in [ 5 ], which does not model the approximate signal in its algorithm. Upon examining the detection performance of both techniques, results show that retaining the approximate signal is better for detecting faults.…”
Section: New Coefficient Selection Criterion and Enhanced Mspca (Emsp...supporting
confidence: 59%
“…Principle component analysis (PCA) is among the most prominent data-based techniques. Multiscale PCA (MSPCA) is a well-established extension of PCA, and it is progressively being used in the process monitoring literature [ 2 , 3 , 4 , 5 ].…”
Section: Introductionmentioning
confidence: 99%
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“…Наиболее распространенным подходом к решению задачи диагностики является использование кластерных процедур для выявления аномалий в наблюдаемых данных. Анализ существующих исследований позволил выделить четыре группы подходов, применяющихся для решения данного класса задач: подходы, основанные на статистических тестах [6], модельный подход [7,8], метрические методы [9] и методы машинного обучения [10,11]. Кроме того, для решения задачи обнаружения аномалий существует возможность создания ансамбля алгоритмов, как правило, принадлежащих разным группам [12].…”
Section: Introductionunclassified