3 research outputs found
Technological troubleshooting based on sentence embedding with deep transformers
AbstractIn nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine
Semantic rules for capability matchmaking in the context of manufacturing system design and reconfiguration
To survive in dynamic markets and meet the changing requirements, manufacturing companies must rapidly design new production systems and reconfigure existing ones. The current designer-centric search of feasible resources from various catalogues is a time-consuming and laborious process, which limits the consideration of many different alternative resource solutions. This article presents the implementation of an automatic capability matchmaking approach and software, which searches through resource catalogues to find feasible resources and resource combinations for the processing requirements of the product. The approach is based on formal ontology-based descriptions of both products and resources and the semantic rules used to find the matches. The article focuses on these rules implemented with SPIN rule language. They relate to 1) inferring and asserting parameters of combined capabilities of combined resources and 2) comparison of the product characteristics against the capability parameters of the resource (combination). The presented case study proves that the matchmaking system can find feasible matches. However, a human designer must validate the result when making the final resource selection. The approach should speed up the system design and reconfiguration planning and allow more alternative solutions be considered, compared with traditional manual design approaches.publishedVersionPeer reviewe
Development of a context-aware internet of things framework for remote monitoring services
Asset management is concerned with the management practices necessary to
maximise the value delivered by physical engineering assets. Internet of Things
(IoT)-generated data are increasingly considered as an asset and the data asset
value needs to be maximised too. However, asset-generated data in practice are
often collected in non-actionable form. Moreover, IoT data create challenges for
data management and processing. One way to handle challenges is to introduce
context information management, wherein data and service delivery are
determined through resolving the context of a service or data request.
This research was aimed at developing a context awareness framework and
implementing it in an architecture integrating IoT with cloud computing for
industrial monitoring services. The overall aim was achieved through a
methodological investigation consisting of four phases: establish the research
baseline, define experimentation materials and methods, framework design and
development, as well as case study validation and expert judgment. The
framework comprises three layers: the edge, context information management,
and application. Moreover, a maintenance context ontology for the framework
has developed focused on modelling failure analysis of mechanical components,
so as to drive monitoring services adaptation. The developed context-awareness
architecture is expressed business, usage, functional and implementation
viewpoints to frame concerns of relevant stakeholders. The developed framework
was validated through a case study and expert judgement that provided
supporting evidence for its validity and applicability in industrial contexts.
The outcomes of the work can be used in other industrially-relevant application
scenarios to drive maintenance service adaptation. Context adaptive services
can help manufacturing companies in better managing the value of their assets,
while ensuring that they continue to function properly over their lifecycle.Manufacturin