Failure analysis and products in a model-based environment

Castet, Jean‐François; Bareh, Magdy; Nunes, Jeffery; Okon, Shira; Garner, Larry; Chacko, Emmy; Izygon, Michel

doi:10.1109/aero.2018.8396736

Cited by 11 publications

(2 citation statements)

References 5 publications

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“…Both research [28,30] introduced SWRL (Semantic Web Rule Language) to improve reasoning performance and provide output to the decision-maker; in both cases, the attention is given to failures that affect the availability attribute, thus embracing the traditional approach of FMECA. Castet et al [32] ontologically modelled both the FMECA and the FTA (Failure Tree Analysis) by defining the main concepts and the relationships between them. The application to two simplified cases, a rover for nighttime operations and a NASA habitable spacecraft, shows that the models allow for a deep understanding of the physical decomposition of the asset, the failure modes in evolution and possible failure causes that are affecting the current functioning of the asset.…”

Section: Review Of Ontological Modelling Of Criticality Analysis In I...mentioning

confidence: 99%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

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Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

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Section: Review Of Ontological Modelling Of Criticality Analysis In I...mentioning

confidence: 99%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

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“…When considering the manufacturing domain, it is of interest to capture the functional impact of asset integrity level on the actual manufacturing process. Castet et al (2018) presented an approach for capturing fault information in a modeling environment using ontology of fault management and a set of plugins designed to automatically extract two reliability artifacts, the FMECA and fault tree. FMECA offers a sound basis upon which to express the organizational and functional association between a manufacturing asset hierarchy and its linkage with the functional integrity of the production facility.…”

Section: Ontologies In Predictive Maintenance and Asset Managementmentioning

confidence: 99%

Ontology-Based Context Modeling in Physical Asset Integrity Management

Al-Shdifat

Emmanouilidis

Khan

et al. 2020

Front. Comput. Sci.

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Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.

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