Ontology-based fault diagnosis for industrial control applications

Melik-Merkumians, Martin; Zoitl, Alois; Moser, Thomas

doi:10.1109/etfa.2010.5641192

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…Identification of Relevant Runtime Failure Types: in the first process step, domain experts have to discuss and identify the relevant runtime failure types that should be detectable using the proposed approach [6]. Based on their experience, the domain experts select the runtime failure types that by now are not easy to detect using traditional methods.…”

Section: Runtime Phasementioning

confidence: 99%

Efficient automation systems engineering process support based on semantic integration of engineering knowledge

et al. 2011

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Modern industrial automation systems engineering (ASE) environments have to accommodate for heterogeneity coming from the engineering disciplines involved, the software tools and their data models, and run-time data collection. In many ASE environments domain experts have to invest considerable effort to bridge the semantic gaps between common project-level engineering concepts and the diverse local data representations.In this paper we discuss the needs for semantic integration and applications of machine-understandable knowledge engineering in three real-world ASE use cases from our industry partners. We provide an evaluation concept with empirical studies to measure the benefits and limitations of the proposed approach compared to the traditional expert-intensive approach. Major result of the initial evaluation is that semantic integration has good potential to make engineering processes more efficient and robust if supported well with user interfaces that end users find usable and useful.

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Section: Runtime Phasementioning

confidence: 99%

Efficient automation systems engineering process support based on semantic integration of engineering knowledge

et al. 2011

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“…For the design time model, we use the P&ID (Piping and Instrumentation Diagram) of the educational model of an industrial process plant located in the Odo-StrugerLaboratory 2 . As described in detail in [7] and [8], the tank model consists of two tanks, T101 and T102, which are situated on different height levels. The lower tank (T101) contains three level sensors (a critical upper level (B114), a lower level (B104), and a critical lower level lever (B113)), a heater (E104), and a temperature sensor (S111).…”

Section: A General Descriptionmentioning

confidence: 99%

“…These scenarios were originally described in [7] and [8], in this paper we used them as example for real-life usage scenarios of the proposed transformation approach for the transformation of design time safety requirements to rules which can be used to enforce these safety requirements at runtime.…”

Section: Exemplary Runtime Scenariosmentioning

confidence: 99%

Enforcing safety requirements for industrial automation systems at runtime position paper

Sunindyo¹,

Melik-Merkumians²,

Moser³

et al. 2011

2011 2nd International Workshop on Requirements@Run.Time

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Abstract-Current industrial automation systems are becoming more and more complex, and typically involve different phases of engineering, such as design time and runtime. System requirements, which are usually elicited during design time by engineers, currently are not sufficiently represented at runtime, like the runtime enforcement of safety requirements for industrial automation systems. Such kind of enforcement usually is very hard to model and predict at design time. Hence, the need exists to capture and manage safety requirements at design time and runtime, since safety requirements of industrial automation systems may lead to high risks if not addressed properly. In this position paper, we introduce a safety requirements enforcement framework and the using of Boilerplates for requirements elicitation and by explicitly modeling the runtime requirements knowledge for further application. We illustrate and evaluate the approach with data from a real-world case study in the area of industrial process systems. Major result was that the Boilerplates and explicit engineering knowledge are well suited to capture and enforce runtime safety requirements of industrial automation systems.

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“…This process is also an important process in the implementation of data integration technology [26]. The semantic data mapping process is the standardization and mapping process to produce uniformity between data with various data representation, heterogeneity format data and different semantic aspect between applications in the different data sources [27][28][29]. In the current days, there are a lot of technologies and tools in term of semantic data mapping process, and this research is to compare and analyze the existing semantic data mapping technology and tools based on several criteria's [23,24,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49].…”

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confidence: 99%

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

Yunianta

Barukab

Yusof

et al. 2017

Int. J. Adv. Intell. Informatics

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The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language.

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Ontology-based fault diagnosis for industrial control applications

Cited by 18 publications

References 7 publications

Efficient automation systems engineering process support based on semantic integration of engineering knowledge

Efficient automation systems engineering process support based on semantic integration of engineering knowledge

Enforcing safety requirements for industrial automation systems at runtime position paper

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

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