NLP Tools for Predictive Maintenance Records in M aint N et (2020)

Abstract: Processing maintenance logbook records is an important step in the development of predictive maintenance systems. Logbooks often include free text fields with domain specific terms, abbreviations, and non-standard spelling posing challenges to off-the-shelf NLP pipelines trained on standard contemporary corpora. Despite the importance of this data type, processing predictive maintenance data is still an under-explored topic in NLP. With the goal of providing more datasets and resources to the community, in this paper we present a number of new resources available in MaintNet, a collaborative open-source library and data repository of predictive maintenance language datasets. We describe novel annotated datasets from multiple domains such as aviation, automotive, and facility maintenance domains and new tools for segmentation, spell checking, POS tagging, clustering, and classification.

## introduction engineering systems are generating ever increasing amounts of maintenance records often recorded in the form of event logbooks. the analysis of these records are aimed to improve predictive maintenance systems reducing maintenance costs, helping to prevent accidents, and saving lives @xcite . predictive maintenance records are collected in multiple domains such as aviation, healthcare, and transportation @xcite . in this paper, we present new datasets in the aviation and automotive domains listed in maintenance record datasets generally contain free text fields describing issues and actions, as in the instances presented in the main contributions of this paper are the following: as to the problem of non-standard spelling, @xcite , proposed a method of correcting misspelled words in clinical records by mapping spelling errors to a large database of correction candidates. however, due to the large number of abbreviations in medical records, they were limited to specific terms and the normalization had to be performed separately. de amorim and zampieri (2013) proposed a dictionary-based spell correction algorithm using a clustering technique by comparing various distance metrics to aim to lower the number of distance calculations while finding or matching target words for misspellings. with this in mind, in maintnet we provide users with tools developed to deal with domain-specific misspellings and abbreviations. ## maintnet features in the next sub-sections we present the tools in resources available in maintnet divided into language resources, pre-processing, and clustering, in addition to that, maintnet provides various dynamic webpages for users to communicate with each other and with the project developers which work similarly to a forum or message board. we hope that maintnet's community participation features will further facilitate discussion and research in this under explored domain. ## conclusion in this paper we evaluate the tools available in maintnet, a collaborative open-source library for predictive maintenance language resources. maint-net provides technical logbook datasets on multiple domains: aviation, automotive, and facility maintenance. a number of other important language resources such as abbreviation lists, morphosyntactic information lists, and termbanks have been developed and are also available through the platform. text (pre-)processing tools developed in python were evaluated and are also made available. these include spell checking, pos tagging, and document clustering. finally, we performed an intrinsic evaluation comparing the performance of several spellcheckers on five of the seven datasets in maintnet and an extrinsic evaluation on raw and processed versions of the avi-main dataset on pos tagging. we showed an important increase in performance for all taggers we tested when using data processed with maintnet's pre-processing pipeline. for the pos tagger comparison and clustering, we developed manually annotated gold standards which are also made available through the platform.