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Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry
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- Löwenmark, Karl (författare)
- Luleå tekniska universitet,EISLAB
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- Taal, Cees (författare)
- SKF Research & Technology Development, Meidoornkade 14, 3992 AE, Houten, P.O. Box 2350, 3430 DT, Nieuwegein, The Netherlands
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- Schnabel, Stephan (författare)
- SKF Condition Monitoring Center Luleå AB, 977 75, Luleå, Sweden
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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- Sandin, Fredrik (författare)
- Luleå tekniska universitet,EISLAB
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(creator_code:org_t)
- 2022-10-25
- 2022
- Engelska.
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Ingår i: International Journal of Prognostics and Health Management. - : Prognostics and Health Management Society. - 2153-2648. ; 13:2
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainabil-ity, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digi-tised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportu-nity to develop technical language supervision (TLS) solu-tions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample gen-eralisation. We surveyed the literature and identify a con-siderable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on Sentence-BERT and contrastive learning based zero-shot inference on annotated industry data.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Intelligent Fault Diagnosis
- Natural Language Supervision
- Technical Language Processing
- Condition Monitoring
- Technical Language Supervision
- Natural Language Processing
- Maskininlärning
- Machine Learning
- Cyberfysiska system
- Cyber-Physical Systems
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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