Machine-interpretable Engineering Design Standards for Valve Specification

Engineering design processes use technical specifications and must comply with standards. Product specifications, product type data sheets, and design standards are still mainly document-centric despite the ambition to digitalize industrial work. In this paper, we demonstrate how to transform information held in engineering design standards into modular, reusable, machine-interpretable ontologies and use the ontologies in quality assurance of the plant design and equipment selection process. We use modelling patterns to create modular ontologies for knowledge captured in the text and in frequently referenced tables in International Standards for piping, material and valve design. These modules are exchangeable, as stored in a W3C compliant format, and interoperable as they are aligned with the top-level ontology ISO DIS 23726-3: Industrial Data Ontology (IDO). We test these ontologies, created based on international material and piping standards and industry norms, on a valve selection process. Valves are instantiated in semantic asset models as individuals along with a semantic representation of the environmental condition at their location on the asset. We create "functional location tags" as OWL individuals that become instances of OWL class Valve Data Sheet (VDS) specified valves. Similarly we create instances of manufacturer product type. Our approach enables automated validation that a specific VDS is compliant with relevant industry standards. Using semantic reasoning and executable design rules, we also determine whether the product type meets the valve specification. Creation of shared, reusable IDO-based modular ontologies for design standards enables semantic reasoning to be applied to equipment selection processes and demonstrates the potential of this approach for Standards Bodies wanting to transition to digitized Smart Standards.

Iof-maint -- Modular maintenance ontology

In this paper we present a publicly-available maintenance ontology (Iof-maint). Iof-maint is a modular ontology aligned with the Industrial Ontology Foundry Core (IOF Core) and contains 20 classes and 2 relations. It provides a set of maintenance-specific terms used in a wide variety of practical data-driven use cases. Iof-maint supports OWL DL reasoning, is documented, and is actively maintained on GitHub. In this paper, we describe the evolution of the Iof-maint reference ontology based on the extraction of common concepts identified in a number of application ontologies working with industry maintenance work order, procedure and failure mode data.

Large Language Models for Failure Mode Classification: An Investigation

In this paper we present the first investigation into the effectiveness of Large Language Models (LLMs) for Failure Mode Classification (FMC). FMC, the task of automatically labelling an observation with a corresponding failure mode code, is a critical task in the maintenance domain as it reduces the need for reliability engineers to spend their time manually analysing work orders. We detail our approach to prompt engineering to enable an LLM to predict the failure mode of a given observation using a restricted code list. We demonstrate that the performance of a GPT-3.5 model (F1=0.80) fine-tuned on annotated data is a significant improvement over a currently available text classification model (F1=0.60) trained on the same annotated data set. The fine-tuned model also outperforms the out-of-the box GPT-3.5 (F1=0.46). This investigation reinforces the need for high quality fine-tuning data sets for domain-specific tasks using LLMs.

Beamforming using Digital Piezoelectric MEMS Microphone Array

The recent explosion in low-cost, low-power wireless microcontrollers, coupled with low-power, robust MEMS sensors has opened up the opportunity to create new forms of low-cost Industrial Internet-of-Things (IIoT) devices for condition monitoring. Piezoelectric MEMS microphones constructed with a cantilever diaphragm are a potential solution against failure modes, such as water and dust ingress, that have challenged the use of capacitive MEMS microphones in industrial applications. In this paper, we couple a pair of piezoelectric MEMS microphones to a COTS microcontroller to create a stand-alone microphone array capable of discerning the direction of a noise source. The microphone array is designed to acquire sound data without aliasing at frequencies of 2000 Hz or below. Testing is conducted in an anechoic chamber. We compare the performance of this microphone array to a simple idealized theoretical model. The experimental results obtained in the anechoic chamber compare well with the theoretical model. The work stands as a proof-of-principle. By providing detailed information on how we coupled the sensors to a COTS microcontroller, and the open-source code used to process the data, we hope that others will be able to build upon this work by expanding on both the number and type of sensors used.