Prognostics and systems health management (PHM) is an enabling discipline that uses sensors to assess the health of systems, diagnoses anomalous behavior, and predicts the remaining useful performance over the life of the asset. The advent of the Internet of Things (IoT) enables PHM to be applied to all types of assets across all sectors, thereby creating a paradigm shift that is opening up significant new business opportunities. This paper introduces the concepts of PHM and discusses the opportunities provided by the IoT. Developments are illustrated with examples of innovations from manufacturing, consumer products, and infrastructure. From this review, a number of challenges that result from the rapid adoption of IoT-based PHM are identified. These include appropriate analytics, security, IoT platforms, sensor energy harvesting, IoT business models, and licensing approaches.

Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of asset management and the valuable information these documents contain is underutilized in analysis. Meanwhile, researchers historically design NLP pipelines with non-technical language in mind. This means industrial implementations are built on tools intended for non-technical use cases, suffering from a lack of verification, validation, and ultimately, personnel trust. To mitigate these sources of risk, we encourage a holistic, domain-driven approach to using NLP in a technical engineering setting, a paradigm we refer to as Technical Language Processing (TLP). Toward this end, the industrial asset management community must collectively redouble efforts toward production of and consensus around key domain-specific resources, including: 1) goal-driven data representations, 2) flexible entity type definitions and dictionaries, and 3) improved access to data-sets – raw and annotated. This collective action allows the maintenance community to follow in the path of other scientific communities, e.g., medicine, to develop and utilize these public resources to make TLP a key contributor to Industry 4.0.

Despite recent dramatic successes, natural language processing (NLP) is not ready to address a variety of real‐world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and large computational resource requirements, makes domain‐specific application of even the most successful NLP techniques difficult. This paper proposes technical language processing (TLP) which brings engineering principles and practices to NLP specifically for the purpose of extracting actionable information from language generated by experts in their technical tasks, systems, and processes. TLP envisages NLP as a socio‐technical system rather than as an algorithmic pipeline. We describe how the TLP approach to meaning and generalization differs from that of NLP, how data quantity and quality can be addressed in engineering technical domains, and the potential risks of not adapting NLP for technical use cases. Engineering problems can benefit immensely from the inclusion of knowledge from unstructured data, currently unavailable due to issues with out of the box NLP packages. We illustrate the TLP approach by focusing on maintenance in industrial organizations as a case‐study.

In this paper, we present a domain-specific, open access, reference ontology (ROMAIN) for the maintenance management domain. We use a hybrid approach, based on a top-down alignment to an open source top-level ontology, the Basic Formal Ontology (BFO)