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In the process industry, condition monitoring systems with automated faultdiagnosis methods assist human experts and thereby improve maintenanceefficiency, process sustainability, and workplace safety. Improving theautomated fault diagnosis methods using data and machine learning-based modelsis a central aspect of intelligent fault diagnosis (IFD). A major challenge inIFD is to develop realistic datasets with accurate labels needed to train andvalidate models, and to transfer models trained with labeled lab data toheterogeneous process industry environments. However, fault descriptions andwork-orders written by domain experts are increasingly digitised in moderncondition monitoring systems, for example in the context of rotating equipmentmonitoring. Thus, domain-specific knowledge about fault characteristics andseverities exists as technical language annotations in industrial datasets.Furthermore, recent advances in natural language processing enable weaklysupervised model optimisation using natural language annotations, most notablyin the form of natural language supervision (NLS). This creates a timelyopportunity to develop technical language supervision (TLS) solutions for IFDsystems grounded in industrial data, for example as a complement topre-training with lab data to address problems like overfitting and inaccurateout-of-sample generalisation. We surveyed the literature and identify aconsiderable improvement in the maturity of NLS over the last two years,facilitating applications beyond natural language; a rapid development of weaksupervision methods; and transfer learning as a current trend in IFD which canbenefit from these developments. Finally we describe a general framework forTLS and implement a TLS case study based on SentenceBERT and contrastivelearning based zero-shot inference on annotated industry data.

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry