Abstract
Asset management is concerned with the management practices, technologies and tools necessary to maximise the value delivered by physical engineering assets. 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. 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 modelling 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, twofold: 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.
Highlights
Typical applications of internet of things (IoT) technologies amalgamate the ability to identify, sense, compute, communicate and sometimes actuate, for the purpose of monitoring and remotely controlling the environment
When users interact with systems in this regard, the proposed maintenance ontology can help them to narrow down the list of options by providing answers to questions such as:
An example of a typical utilization scenario is that during condition monitoring queries could be raised to resolve the monitoring service context. This could be related to determining the failure modes of a part, the functional effect of a defect on the operation of the test rig, the measurement alternatives suitable for specific defects and parts, in addition to the relevant measurement parameters
Summary
Typical applications of internet of things (IoT) technologies amalgamate the ability to identify, sense, compute, communicate and sometimes actuate, for the purpose of monitoring and remotely controlling the environment (de Matos et al, 2020). (2020), it is predicted that the amount of devices with Internet connectivity will exceed 50 billion by 2030 Such devices produce significant volumes of data which are communicated through networks, and upon processing enable better informed decision making and actions. Systems with context awareness are employed within IoT environments for the purpose of sensing the operational environment and for delivering an appropriate response to both the user and application (Perera et al, 2014). Such systems are capable of analyzing the data generated by IoT devices, generating a high-level of semantic organization of data and converting it into
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