Generalized Pattern Matching of Industrial Alarm Flood Sequences via Word Processing and Sequence Alignment

Zhou, Boyuan; Hu, Wenkai; Brown, Kevin; Chen, Tongwen

doi:10.1109/tie.2020.3026287

Cited by 18 publications

(3 citation statements)

References 28 publications

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“…Thus, human errors in industrial applications have been minimized and made more stable. Many studies have been done in the literature on the subject (Kamtongdee et al, 2013;Hengdi et al, 2011;Panoiu et al, 2015;Hryniewicz et al, 2015;Patil and Ingle, 2020;Koniar et al, 2014;Rouget et al, 2018;Kalina and Golovanov, 2019;Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the study, an optical character recognition (OCR) algorithm based on template matching definition, which has adaptive binarization feature and does not require many training examples, is presented (Kalina and Golovanov, 2019). In the study, a systematic two-step model matching method is proposed to catch similar alarm floods in different processes (Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Endüstriyel uygulamalarda güvenilir bir kalite kontrolü için yapay sinir ağı kullanan gerçek zamanlı bir desen eşleştirme algoritmasının geliştirilmesi

Güzelce¹,

Bayrak²

2021

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Today, making quality control systems with reliable accuracy is very important in producing industrial products with zero defects. In this respect, it is an essential issue that camera control systems work with reliable control algorithms. In this study, a real-time control algorithm using a pattern matching algorithm has been developed to optimize the minimum contrast parameter with an Artificial Neural Network (ANN). In the study, the comparison of three algorithms included in pattern matching in terms of time was made using LabVIEW image control tools. Besides, one of the most critical parameters in the low-discrepancy sampling algorithm, which gives good results in time, minimum contrast parameter is discussed. The optimization of this parameter is done by using the Levenberg-Marquardt training algorithm in ANN. The obtained results show that the proposed pattern matching algorithm using ANN for optimizing the minimum contrast parameter is fast and effective for quality control applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Endüstriyel uygulamalarda güvenilir bir kalite kontrolü için yapay sinir ağı kullanan gerçek zamanlı bir desen eşleştirme algoritmasının geliştirilmesi

Güzelce¹,

Bayrak²

2021

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…To improve the computational efficiency, an accelerated alarm flood sequence alignment method was proposed in [5]. Toward early warning of alarm floods, a real-time pattern matching method was developed in [6] to provide a ranking list of similar historical floods for operators during online monitoring. Reference [7] formulated a generalized pattern matching approach for comparison of alarm floods generated from several individual facilities that are similar in architecture and functionality.…”

mentioning

confidence: 99%