Technical language processing: Unlocking maintenance knowledge

Brundage, Michael P.; Sexton, Thurston; Hodkiewicz, Melinda; Dima, Alden A.; Lukens, Sarah

doi:10.1016/j.mfglet.2020.11.001

Cited by 73 publications

(45 citation statements)

References 16 publications

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“…In place of the aesthetic of stringing together complex algorithmic black boxes and hoping for the desired outcome, TLP encourages human intervention to inject domain knowledge and meaning at each stage of the analysis as detailed in our previous paper. 21 This can help mitigate the accumulation of systemic technical bias in the final analysis. By adapting NLP to focus on the challenges of engineering text, TLP can bring the promise of text analysis to industry.…”

Section: Discussionmentioning

confidence: 99%

“…In the past, records were kept on paper, but are nowadays stored in unstructured text fields in relational database systems and spreadsheets. These MWO records are akin to medical records for an individual, 21 and are vital to efforts that estimate the reliability of the asset and potential for functional failures. However, there are a number of challenges in extracting knowledge from these texts.…”

Section: Maintenance Records and Textmentioning

confidence: 99%

“…10 On the surface, these resources can help mitigate the lexical variations in technical text and simplify domain adaptation between similar technical domains by constraining terminological variation to the intrinsic differences found between facilities. 21 But from a deeper perspective, they provide a source of standardized fortuitous data; they represent shared knowledge that can be used to understand the latent variables associated with a domain and help define the proper context for their interpretation. This knowledge can also help with the comparison of domains and further foster the sharing and adaptation of analysis approaches.…”

Section: Making Use Of Fortuitous Datamentioning

confidence: 99%

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Adapting natural language processing for technical text

Dima

Lukens²,

Hodkiewicz

et al. 2021

Applied AI Letters

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Maintenance Records and Textmentioning

confidence: 99%

Section: Making Use Of Fortuitous Datamentioning

confidence: 99%

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Adapting natural language processing for technical text

Dima

Lukens²,

Hodkiewicz

et al. 2021

Applied AI Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…This hybrid approach of labor intensive expert manual tagging and subsequent automated tagging is best suited to maintenance log data [20]. In contrast to traditional NLP applications such as named entity recognition or POS tagging that have very large corpora of documents for which several NLP libraries (such as SpaCy, NLTK, and so on) have been built to automate the process, maintenance data are usually much fewer (less than 10,000 rows) [33], as equipment failure is a relatively rare event. Additionally, the logs contain domain specific technical terms that traditional NLP pipelines fall short of processing [33].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Ademujimi

Prabhu

2021

Sensors

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Bayesian Network (BN) models are being successfully applied to improve fault diagnosis, which in turn can improve equipment uptime and customer service. Most of these BN models are essentially trained using quantitative data obtained from sensors. However, sensors may not be able to cover all faults and therefore such BN models would be incomplete. Furthermore, many systems have maintenance logs that can serve as qualitative data, potentially containing historic causation information in unstructured natural language replete with technical terms. The motivation of this paper is to leverage all of the data available to improve BN learning. Specifically, we propose a method for fusion-learning of BNs: for quantitative data obtained from sensors, metrology data and qualitative data from maintenance logs, corrective and preventive action reports, and then follow by fusing these two BNs. Furthermore, we propose a human-in-the-loop approach for expert knowledge elicitation of the BN structure aided by logged natural language data instead of relying exclusively on their anecdotal memory. The resulting fused BN model can be expected to provide improved diagnostics as it has a wider fault coverage than the individual BNs. We demonstrate the efficacy of our proposed method using real world data from uninterruptible power supply (UPS) fault diagnostics.

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“…The data in this project is provided by a mining company for work orders describing maintenance work on their pumps. As described in [3], [26], Maintenance work orders are records with a number of fields including creation date, work required, type of work (corrective, preventative etc. ), desired start and end dates, costs and other information relevant to the planning and execution of maintenance work.…”

Section: Introductionmentioning

confidence: 99%

Detecting Asset Cascading Failures Using Complex Network Analysis

Moffatt

Zaitouny²,

Hodkiewicz

et al. 2021

IEEE Access

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Experienced process plant personnel observe that corrective maintenance work on one asset may often be followed by corrective work on the same asset or connected assets within a short amount of time. This problem is referred to as a cascading failure. Confirming if these events are chronic is difficult given the number of assets and the volume of maintenance and operation data. If cascading events can be identified, preventative measures can be implemented to prevent those cascades, eliminating unnecessary corrective work. This project uses complex network analysis to identify cascading events and where cooccurrence of work is most frequent, in a process plant. Data is drawn from over 50,000 work orders for 5,655 pumps in a mining company over a five-year period. A complex network is produced by connecting assets based on the frequency of co-occurrence of work. Beside the advantages of the visualisation of complex networks, the method produces quantified measures, normalised degree, eigenvector centrality and betweenness centrality, which are used to identify assets with significant impact on other assets. Affected pumps are apparent as communities in the network. This analysis identifies pumps that are "superspreaders": pumps who experience corrective maintenance events which lead to corrective maintenance events on other pumps. The model can be tuned to different time windows, for example events within one or seven days. From these insights, changes can be made to operational, maintenance and recording practices to prevent re-occurrence. Of particular note in this data was the occurrence of self-loops in certain pumps and the prevalence of hidden failures in standby pumps.

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Technical language processing: Unlocking maintenance knowledge

Cited by 73 publications

References 16 publications

Adapting natural language processing for technical text

Adapting natural language processing for technical text

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Detecting Asset Cascading Failures Using Complex Network Analysis

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