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Reliability engineering based on operating data and monitoring systems within technical products
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
S. Bracke, M. Hinz, C. van Gulijk, F. Gronwald, M. Muenker, M. Inoue, S. Yamada, E. Patelli, B. Ulutas, M. Bonato, T. Yamada
To some extent, the requirements for data are relatively flexible as long as that data supports the data-scheme for monitoring systems. The requirements therefore focus on data scheme and depend less on the actual data itself. The objective is to enable communication between the data-sender and the data-receiver. The data sender, say a logging system, has a data scheme to store relevant information in its own local database (which may be small if there are detectors only). The data may be system state messages, error messages or alarms that may contain a timestamp, serial numbers, identification codes, numeric values and meta-data. The date receiver, say a Matlab application for reliability engineering, has its own data scheme. Only part of the data from the data logger is useful for the Matlab application; some kind of data transformation is required. Such transformations can be made in many different ways; the key, however, is that the meta-data about the data-scheme is correct, informative and up-to-date. In many industries data standards have been developed to harmonize efforts of different industry partners; this tends to be efficient for many industries. For instance, the Oil and Gas industry uses ISO 15926 as an International Standard for the representation of process plant life-cycle information. This standard specifies a generic, conceptual data model that is suitable as the basis for implementation in a shared database or data warehouse. Many industries have developed similar standards; aligning with them in own field is well-worth the effort; IT solutions that do not follow the standards may not be accepted in the industry. Summarizing, data requirements focus on correct data scheme descriptions. For engineers, such descriptions are captured in technical reports. For the computer it is captured in the format of database.
A study on structural CAD data conversion between AVEVA MARINE® and Intergraph Smart 3D®
Published in Ships and Offshore Structures, 2022
Young-Soo Han, KyungHo Lee, JungMin Lee, JaeJun Lee, ByeongWook Nam
A previous research on the utilisation of the international-standards-based neutral format examined the systematic use (Han et al. 2019) and reuse of the design model over the entire life cycle of the product (Kim et al. 2017), and an applied-information-exchange study of the web-service-technology model investigated the exchange of information between the international standard (ISO 15926 Part 7) (Kim et al. 2008) and utilisation of CAD models as CAE models using a concept of ontology (Gujarathi and Ma 2011). A case study using international standards for model-information exchange between shipbuilding/plant CAD systems can divide into the exchange of shape information using STEP, and the exchange of plant models using ISO 15926 standard. Pipe-model conversion (Li et al. 2011) and catalog piping equipment conversion from AM to S3D (Hwang et al. 2004) conducted based on ISO 15926 format data exchange. Additionally, a study was conducted to benchmark interoperability between the commercial mechanical CAD systems (Gerbino and Brondi 2004). Another approach is to expand CAD information to digital mock-up using JT and STEP (Katzenbach et al. 2013), and the study of design information utilisation in metal formation using STEP (Holland et al. 2002). A considerable part of the research using the standard format primarily focused on the utilisation of information of the mechanical CAD.
Application of the Internet of Things in the textile industry
Published in Textile Progress, 2019
Hitesh Manglani, George L. Hodge, William Oxenham
The biggest technical challenge in IoT which machine manufacturers and textile mills both face, concerns heterogeneity and interoperability due to variegated communication protocols and products as confirmed in this review by market analysis of IoT for the textile industry and the case-specific example of the spinning industry. That challenge is not insuperable. This review for the textile industry recommends the use of MQTT, CoAP, and DDS in the application layer, use of RPL & 6LoWPAN in the network layer, and IPv6, EPCglobal in the perception layer. The following protocols could be used to overcome heterogeneity between the exchange of data: IEC 61512 BatchML, IEC 62264 B2MML, ISO 15926 XMplant, IEC 62424 CAEX, IEC 62714 AutomationML, OPC UA’s Data Model, and MTConnect, although each end-case scenario is different. The current issue of Textile Progress should however help practitioners to understand what is offered by the various standards and protocols available.
Construction of injury process from Japanese consumer product narrative injury data using an ontology-based method
Published in International Journal of Injury Control and Safety Promotion, 2023
Xiaodong Feng, Kun Zhang, Fang Jiang, Yoshiki Mikami
Ontology for describing an injury/accident event or as a framework for accident databases has been developed in previous studies. For example, Batres et al. (2009) proposed an approach for accident database development based on ontology techniques. This approach allowed the lack of semantic relations between two types of things to be added to improve accident case query performance. Afterwards, Batres et al. (2014) considered the ISO 15926-2:2003 standard as an upper ontology to describe accident information for data integration. In the study, a past accident was encoded with ontologies and graphically represented to enhance the ability of accident databases to find information. Wu et al. (2020) developed an ontological model using a natural language processing (NLP) technique to represent a metro accident case. Similarly, Single et al. (2020) created an ontology framework for chemical accident databases. The authors identified cause-effect relationships and represented them by an ontology for accident case entries to improve information retrieval. Akagi (2017) proposed an ontology to structurally describe traffic accident data to forecast the accident occurrence probability in an intersection. Sonfack et al. (2023) established a base ontology for accident expertise knowledge to describe how an accident happened and identify its causes and consequences. They concluded that the ontology-based representation of accident expertise knowledge is adequate for integrating, searching, and designing expertise aid systems. Our study differs from theirs in that accident causation models are combined instead of competency questions in the ontology design.