Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond

Jiang, Yuchen; Yin, Shen; Kaynak, Okyay

doi:10.1109/access.2018.2866403

Cited by 220 publications

(69 citation statements)

References 67 publications

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“…Zida et al [27] propose solutions to the problem of identifying high utility sequential rules. Advances in data-driven monitoring and cyber-physical systems [28][29][30] also highlight the importance of utility-based pattern mining.…”

Section: Literature Reviewmentioning

confidence: 99%

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Buffett

2020

Information

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A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern mining relies on the downward closure property to significantly reduce the required search space, with HUSPM, this property does not hold. To address this drawback, an approach is proposed that establishes a tight upper bound on the utility of future candidate sequential patterns by maintaining a list of items that are deemed potential candidates for concatenation. Such candidates are provably the only items that are ever needed for any extension of a given sequential pattern or its descendants in the search tree. This list is then exploited to significantly further tighten the upper bound on the utilities of descendent patterns. An extension of this work is then proposed that significantly reduces the computational cost of updating database utilities each time a candidate item is removed from the list, resulting in a massive reduction in the number of candidate sequential patterns that need to be generated in the search. Sequential pattern mining methods implementing these new techniques for bound reduction and further candidate list reduction are demonstrated via the introduction of the CRUSP and CRUSPPivot algorithms, respectively. Validation of the techniques was conducted on six public datasets. Tests show that use of the CRUSP algorithm results in a significant reduction in the overall number of candidate sequential patterns that need to be considered, and subsequently a significant reduction in run time, when compared to the current state of the art in bounding techniques. When employing the CRUSPPivot algorithm, the further reduction in the size of the search space was found to be dramatic, with the reduction in run time found to be dramatic to moderate, depending on the dataset. Demonstrating the practical significance of the work, experiments showed that time required for one particularly complex dataset was reduced from many hours to less than one minute.

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Section: Literature Reviewmentioning

confidence: 99%

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Buffett

2020

Information

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“…The digitization introduced around the 1970s helped use advanced electronics and information technology for process automation, which formed the era of Industry 3.0. The current (r)evolution, termed Industry 4.0, is driven by the emergence of concepts, such as cloud-computing, smart sensors, internet of things (IoT), big data analytics, augmented reality, and human-machine interfaces [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

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Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

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“…In this sense real and virtual systems are connected by means of data, which are then transformed into information and eventually into knowledge, to be able to take support management decisions [3]. While CPSs exist in common human daily life (for example, car sharing), Industrial CPS (ICPS) have special focus on novel approaches to system monitoring, fault diagnosis and control, aiming at achieving plant-wide intelligent manufacturing [4].…”

Section: Introductionmentioning

confidence: 99%

“…The concept is highlighted in Figure 1, representing the outcome of academic research on Big Data usage in the timespan 2010-2016, in which manufacturing accounts only for 8% [9]. Industrial CPS (ICPS) have special focus on novel approaches to system monitoring, fault diagnosis and control, aiming at achieving plant-wide intelligent manufacturing [4]. The major role in collecting and processing data is played by Internet of Things (IoT) and Big Data Analytics, on which both academic and business communities are spending a considering effort.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the nature of available data, as deeply discussed in Section 3.2, prevents the creation of models based on signals coming from sensors and describing machines technical parameters such as temperatures, absorbed powers or vibrations. This concept explains why powerful and reliable techniques provided by ICPS, such as data-driven KPI estimators and predictors, based on real or soft sensors [4], are not completely applicable to this specific context.…”

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confidence: 99%

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Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems

Storti¹,

Cattaneo

Polenghi

et al. 2018

Machines

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The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too.

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Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond

Cited by 220 publications

References 67 publications

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems

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