Technical Language Supervision for Intelligent Fault Diagnosis in
Process Industry
- URL: http://arxiv.org/abs/2112.07356v1
- Date: Sat, 11 Dec 2021 18:59:40 GMT
- Title: Technical Language Supervision for Intelligent Fault Diagnosis in
Process Industry
- Authors: Karl L\"owenmark, Cees Taal, Stephan Schnabel, Marcus Liwicki, and
Fredrik Sandin
- Abstract summary: In the process industry, condition monitoring systems with automated fault diagnosis methods assisthuman experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.
A major challenge in intelligent fault diagnosis (IFD) is to develop realistic datasets withaccurate labels needed to train and validate models.
domain-specific knowledge about fault characteristics and severitiesexists as technical language annotations in industrial datasets.
This creates a timely opportunity to developtechnical language supervision(TLS) solutions for IFD systems grounded in industrial data.
- Score: 1.8574771508622119
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the process industry, condition monitoring systems with automated fault
diagnosis methods assisthuman experts and thereby improve maintenance
efficiency, process sustainability, and workplace safety.Improving the
automated fault diagnosis methods using data and machine learning-based models
is a centralaspect of intelligent fault diagnosis (IFD). A major challenge in
IFD is to develop realistic datasets withaccurate labels needed to train and
validate models, and to transfer models trained with labeled lab datato
heterogeneous process industry environments. However, fault descriptions and
work-orders written bydomain experts are increasingly digitized in modern
condition monitoring systems, for example in the contextof rotating equipment
monitoring. Thus, domain-specific knowledge about fault characteristics and
severitiesexists as technical language annotations in industrial datasets.
Furthermore, recent advances in naturallanguage processing enable weakly
supervised model optimization using natural language annotations, mostnotably
in the form ofnatural language supervision(NLS). This creates a timely
opportunity to developtechnical language supervision(TLS) solutions for IFD
systems grounded in industrial data, for exampleas a complement to pre-training
with lab data to address problems like overfitting and inaccurate out-of-sample
generalisation. We surveyed the literature and identify a considerable
improvement in the maturityof NLS over the last two years, facilitating
applications beyond natural language; a rapid development ofweak supervision
methods; and transfer learning as a current trend in IFD which can benefit from
thesedevelopments. Finally, we describe a framework for integration of TLS in
IFD which is inspired by recentNLS innovations.
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