Labelling of Annotated Condition Monitoring Data Through Technical Language Processing



Published Oct 26, 2023
Karl Lowenmark Cees Taal Amit Vurgaft Joakim Nivre Marcus Liwicki Fredrik Sandin


We propose a novel approach to facilitate supervised fault diagnosis on unlabelled but annotated industry datasets using human-centric technical language processing and weak supervision. Fault diagnosis through Condition Monitoring (CM) is vital for high safety and resource efficiency in the green transition and digital transformation of the process industry. Learning-based Intelligent Fault Diagnosis (IFD) methods are required to automate maintenance decisions and improve decision support for analysts. A major challenge is the lack of labelled industry datasets, limiting supervised IFD research to lab datasets. However, features learned from lab environments generalise poorly to field environments due to different signal distributions, artificial induction or acceleration of lab faults, and lab set-up properties such as average frequency profiles affecting learned features. In this study, we investigate how the unstructured free text fault annotations and maintenance work orders that are present in many industrial CM systems can be used for IFD through technical language processing, based on recent advances in natural language supervision. We introduce two distinct pipelines, one based on contrastive pre-training on large datasets, and one based on a small-data human-centric approach with unsupervised clustering methods. Finally, we showcase one example of the small-data fault classification implementation on a CM industry dataset with a SentenceBERT language model, kMeans clustering, and conventional signal processing methods. Fault class imbalance and time-shift uncertainty is overcome with weak supervision through aggregates of features, and human-centric clustering is used to integrate technical knowledge with the annotation-based fault classes. We show that our model can separate cable and sensor fault recordings from bearing-related fault recordings with an F1-score of 93. To our knowledge, this is the first system to classify faults in field industry CM data based only on associated unstructured fault annotations.

How to Cite

Lowenmark, K., Taal, C., Vurgaft, A., Nivre, J., Liwicki, M., & Sandin, F. (2023). Labelling of Annotated Condition Monitoring Data Through Technical Language Processing. Annual Conference of the PHM Society, 15(1).
Abstract 107 | PDF Downloads 87



Intelligent Fault Diagnosis, Technical Language Processing, Natural Language Processing, Condition Monitoring, Technical Language Supervision, Natural Language Supervision, Prognostics and Health Management, Industry Data

[1]J. Acs, A. Kornai, Evaluating embeddings on dictionary-based similarity, in: 2016: pp. 78–82.
[2]F. Akhbardeh, C.O. Alm, M. Zampieri, T. Desell, Handling Extreme Class Imbalance in Technical Logbook Datasets, in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021: pp. 4034–4045.
[3]F. Akhbardeh, T. Desell, M. Zampieri, MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources, in: Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, International Committee on Computational Linguistics (ICCL), Barcelona, Spain (Online), 2020: pp. 7–11.
[4]P. Anderson, B. Fernando, M. Johnson, S. Gould, SPICE: Semantic Propositional Image Caption Evaluation, 2016.
[5]P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, L. Zhang, Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: pp. 6077–6086.
[6]K. Arif-Uz-Zaman, M.E. Cholette, L. Ma, A. Karim, Extracting failure time data from industrial maintenance records using text mining, Adv. Eng. Informatics. 33 (2017) 388–396.
[7]D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, 2016.
[8]A. Bakarov, A Survey of Word Embeddings Evaluation Methods, 2018.
[9]S. Banerjee, A. Lavie, METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments, in: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, Association for Computational Linguistics, Ann Arbor, Michigan, 2005: pp. 65–72.
[10]M. Baroni, G. Dinu, G. Kruszewski, Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors, in: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Baltimore, Maryland, 2014: pp. 238–247.
[11]M. Batchkarov, T. Kober, J. Reffin, J. Weeds, D. Weir, A critique of word similarity as a method for evaluating distributional semantic models, in: Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, 2016: pp. 7–12.
[12]T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D.M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language Models are Few-Shot Learners, 2020.
[13]M.P. Brundage, K.C. Morris, T. Sexton, S. Moccozet, M. Hoffman, Developing Maintenance Key Performance Indicators From Maintenance Work Order Data, Volume 3: Manufacturing Equipment and Systems. (2018).
[14]M.P. Brundage, T. Sexton, M. Hodkiewicz, A. Dima, S. Lukens, Technical language processing: Unlocking maintenance knowledge, Manufacturing Letters. 27 (2021) 42–46.
[15]M.P. Brundage, T. Sexton, M.R. Hodkiewicz, K.C. Morris, J.F. Arinez, F. Ameri, J. Ni, G. Xiao, Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing, Journal of Manufacturing Science and Engineering. (2019).
[16]M.P. Brundage, M. Sharp, R.F. Pavel, Qualifying Evaluations from Human Operators: Integrating Sensor Data with Natural Language Logs, in: 2021.
[17]G. Brunner, Y. Wang, R. Wattenhofer, M. Weigelt, Disentangling the Latent Space of (Variational) Autoencoders for NLP, in: UKCI, 2018.
[18]J.P.U. Cadavid, B. Grabot, S. Lamouri, R. Pellerin, A. Fortin, Valuing free-form text data from maintenance logs through transfer learning with CamemBERT, Enterprise Information Systems. 16 (2020).
[19]J.P.U. Cadavid, S. Lamouri, B. Grabot, A. Fortin, Using deep learning to value free-form text data for predictive maintenance, International Journal of Production Research. 60 (2022) 4548–4575.
[20]J.P.U. Cadavid, S. Lamouri, B. Grabot, R. Pellerin, A. Fortin, Estimation of Production Inhibition Time Using Data Mining to Improve Production Planning and Control, 2019 International Conference on Industrial Engineering and Systems Management (IESM). (2019) 1–6.
[21]E.P. Carden, P. Fanning, Vibration Based Condition Monitoring: A Review, Structural Health Monitoring. 3 (2004) 355–377.
[22]D. Catenazzo, B. O’Flynn, M. Walsh, On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0, in: 2018 12th International Conference on Sensing Technology (ICST), 2018: pp. 256–262.
[23]N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence Research. 16 (2002) 321–357.
[24]T. Chen, S. Kornblith, M. Norouzi, G. Hinton, A simple framework for contrastive learning of visual representations, in: 37th International Conference on Machine Learning, ICML 2020, 2020: pp. 1575–1585.
[25]E. Chersoni, L. Pannitto, E. Santus, A. Lenci, C.-R. Huang, Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?, in: Proceedings of the 12th Language Resources and Evaluation Conference, European Language Resources Association, Marseille, France, 2020: pp. 5708–5713.
[26]A. Conte, C. Bolland, L. Phan, M. Brundage, T. Sexton, The Impact of Data Quality on Maintenance Work Order Analysis: A Case Study in HVAC Work Durations, in: 2021.
[27]J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: pp. 248–255.
[28]K. Desai, J. Johnson, VirTex: Learning Visual Representations from Textual Annotations, arXiv, 2020.
[29]J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019.
[30]A. Dima, S. Lukens, M.R. Hodkiewicz, T. Sexton, M.P. Brundage, Adapting natural language processing for technical text, Applied AI Letters. (2021).
[31]A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv, 2020.
[32]K.J. Duncan, C. Pattamadilok, I. Knierim, J.T. Devlin, Consistency and variability in functional localisers, Neuroimage. 46 (2009) 1018–1026.
[33]B. Edwards, M. Zatorsky, R. Nayak, Clustering and Classification of Maintenance Logs Using Text Data Mining, in: Proceedings of the 7th Australasian Data Mining Conference - Volume 87, Australian Computer Society, Inc., Glenelg, Australia, 2008: pp. 193–199.
[34]M. Faruqui, Y. Tsvetkov, P. Rastogi, C. Dyer, Problems With Evaluation of Word Embeddings Using Word Similarity Tasks, 2016.
[35]H.H. Friedman, T. Amoo, Rating the rating scales, Friedman, Hershey H. and Amoo, Taiwo (1999)." Rating the Rating Scales." Journal of Marketing Management, Winter. (1999) 114–123.
[36]A. Fürst, E. Rumetshofer, J. Lehner, V. Tran, F. Tang, H. Ramsauer, D. Kreil, M. Kopp, G. Klambauer, A. Bitto-Nemling, S. Hochreiter, CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP, arXiv, 2021.
[37]Y. Gao, C. Woods, W. Liu, T. French, M.R. Hodkiewicz, Pipeline for Machine Reading of Unstructured Maintenance Work Order Records, in: 2020.
[38]J.-B. Grill, F. Strub, F. Altché, C. Tallec, P.H. Richemond, E. Buchatskaya, C. Doersch, B.A. Pires, Z.D. Guo, M.G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, M. Valko, Bootstrap your own latent: A new approach to self-supervised Learning, arXiv, 2020.
[39]N. Gurnani, Hypothesis Testing based Intrinsic Evaluation of Word Embeddings, in: Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, Association for Computational Linguistics, Copenhagen, Denmark, 2017: pp. 16–20.
[40]E.D. Gutiérrez, R. Levy, B. Bergen, Finding non-arbitrary form-meaning systematicity using string-metric learning for kernel regression, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016: pp. 2379–2388.
[41]E.M. Hastings, T. Sexton, M.P. Brundage, M.R. Hodkiewicz, Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining, Annual Conference of the PHM Society. (2019).
[42]H. He, E.A. Garcia, Learning from Imbalanced Data, IEEE Transactions on Knowledge and Data Engineering. 21 (2009) 1263–1284.
[43]K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[44]F. Hill, R. Reichart, A. Korhonen, SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation, Computational Linguistics. 41 (2015) 665–695.
[45]V.J. Hodge, S. O’Keefe, M. Weeks, A. Moulds, Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey, IEEE Transactions on Intelligent Transportation Systems. 16 (2015) 1088–1106.
[46]M.R. Hodkiewicz, M.T. Ho, Cleaning historical maintenance work order data for reliability analysis, Journal of Quality in Maintenance Engineering. 22 (2016) 146–163.
[47]MD.Z. Hossain, F. Sohel, M.F. Shiratuddin, H. Laga, A Comprehensive Survey of Deep Learning for Image Captioning, ACM Comput. Surv. 51 (2019).
[48]L. Huang, Y.L. Murphey, Text Mining with Application to Engineering Diagnostics, in: M. Ali, R. Dapoigny (Eds.), Advances in Applied Artificial Intelligence, Springer Berlin Heidelberg, Berlin, Heidelberg, 2006: pp. 1309–1317.
[49]Z. Huang, Y. Bao, B. Dong, E. Zhou, W. Zuo, W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection, arXiv, 2022.
[50]K.A. Hutchison, D.A. Balota, J.H. Neely, M.J. Cortese, E.R. Cohen-Shikora, C.-S. Tse, M.J. Yap, J.J. Bengson, D. Niemeyer, E. Buchanan, The semantic priming project, Behavior Research Methods. 45 (2013) 1099–1114.
[51]N. Iyer, N. Virani, Z. Yang, A. Saxena, Mixed Initiative Approach for Reliable Tagging of Maintenance Records with Machine Learning, in: Annual Conference of the PHM Society, 2022.
[52]C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q.V. Le, Y. Sung, Z. Li, T. Duerig, Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision, (2021).
[53]P. Kadlec, B. Gabrys, S. Strandt, Data-driven Soft Sensors in the process industry, Computers & Chemical Engineering. 33 (2009) 795–814.
[54]W. Kim, B. Son, I. Kim, ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision, in: ICML, 2021.
[55]M. Köper, C. Scheible, S. Schulte im Walde, Multilingual Reliability and “Semantic” Structure of Continuous Word Spaces, in: Proceedings of the 11th International Conference on Computational Semantics, Association for Computational Linguistics, London, UK, 2015: pp. 40–45.
[56]A. Kornai, M. Kracht, Lexical Semantics and Model Theory: Together at Last?, in: ACL, 2015.
[57]A. Krebs, D. Paperno, Capturing Discriminative Attributes in a Distributional Space: Task Proposal, in: Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, Association for Computational Linguistics, Berlin, Germany, 2016: pp. 51–54.
[58]V.D. Lai, T.N. Nguyen, T.H. Nguyen, Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, 2020: pp. 5405–5411.
[59]Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, A.K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap, Mechanical Systems and Signal Processing. 138 (2020) 106587.
[60]O. Levy, Y. Goldberg, Linguistic Regularities in Sparse and Explicit Word Representations, in: Proceedings of the Eighteenth Conference on Computational Natural Language Learning, Association for Computational Linguistics, Ann Arbor, Michigan, 2014: pp. 171–180.
[61]J. Li, X. Chen, E. Hovy, D. Jurafsky, Visualizing and Understanding Neural Models in NLP, 2016.
[62]J. Li, D. Li, C. Xiong, S. Hoi, Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation, in: International Conference on Machine Learning, PMLR, 2022: pp. 12888–12900.
[63]W. Li, R. Huang, J. Li, Y. Liao, Z. Chen, G. He, R. Yan, K. Gryllias, A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges, Mechanical Systems and Signal Processing. 167 (2022) 108487.
[64]X. Li, X. Yin, C. Li, P. Zhang, X. Hu, L. Zhang, L. Wang, H. Hu, L. Dong, F. Wei, Y. Choi, J. Gao, Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks, in: ECCV, 2020.
[65]S. Liu, P.-T. Bremer, J.J. Thiagarajan, V. Srikumar, B. Wang, Y. Livnat, V. Pascucci, Visual Exploration of Semantic Relationships in Neural Word Embeddings, IEEE Transactions on Visualization and Computer Graphics. 24 (2018) 553–562.
[66]F.F. Liza, M. Grzes, An improved crowdsourcing based evaluation technique for word embedding methods, in: Association for Computational Linguistics, 2016. [67]D. Lou, Z. Liao, S. Deng, N. Zhang, H. Chen, MLBiNet: A Cross-Sentence Collective Event Detection Network, in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", Association for Computational Linguistics, Online, 2021: pp. 4829–4839.
[68]K. Lowenmark, C. Taal, J. Nivre, M. Liwicki, F. Sandin, Processing of Condition Monitoring Annotations with BERT and Technical Language Substitution: A Case Study, in: PHM Society European Conference, 2022: pp. 306–314.
[69]K. Löwenmark, C. Taal, S. Schnabel, M. Liwicki, F. Sandin, Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry, ArXiv E-Prints. (2021) arXiv:2112.07356.
[70]S. Lukens, M. Naik, K. Saetia, X. Hu, Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics, Annual Conference of the PHM Society. (2019).
[71]L. Van der Maaten, G. Hinton, Visualizing data using t-SNE., Journal of Machine Learning Research. 9 (2008).
[72]T. Madeira, R. Melicio, D. Valerio, L. Santos, Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports, Aerospace. 8 (2021).
[73]S. Mallat, S. Zhong, others, Characterization of signals from multiscale edges, IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (1992) 710–732.
[74]S. Manikandan, K. Duraivelu, Fault diagnosis of various rotating equipment using machine learning approaches – A review, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 235 (2021) 629–642.
[75]T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient Estimation of Word Representations in Vector Space, 2013.
[76]N. Mu, A. Kirillov, D. Wagner, S. Xie, SLIP: Self-supervision Meets Language-Image Pre-training, in: S. Avidan, G. Brostow, M. Cissé, G.M. Farinella, T. Hassner (Eds.), Computer Vision – ECCV 2022, Springer Nature Switzerland, Cham, 2022: pp. 529–544.
[77]A.V. Nandyala, S. Lukens, S. Rathod, P. Agarwal, Evaluating word representations in a technical language processing pipeline, in: PHM Society European Conference, 2021: pp. 17–17.
[78]S.M.R. Naqvi, C. Varnier, J.M. Nicod, Noureddine, Zerhouni, M. Ghufran, Leveraging Free-form Text in Maintenance Logs Through BERT Transfer Learning, in: 2022.
[79]M. Navinchandran, M. Sharp, M.P. Brundage, T. Sexton, Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data, Annual Conference of the PHM Society. (2019).
[80]M. Navinchandran, M. Sharp, M.P. Brundage, T. Sexton, Discovering critical KPI factors from natural language in maintenance work orders, Journal of Intelligent Manufacturing. 33 (2022) 1859–1877.
[81]M. Nissim, R. van Noord, R. van der Goot, Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor, Computational Linguistics. 46 (2020) 487–497.
[82]M.V. Ottermo, S. H\aabrekke, S. Hauge, L. Bodsberg, Technical Language Processing for Efficient Classification of Failure Events for Safety Critical Equipment, in: 2021.
[83]K. Papineni, S. Roukos, T. Ward, W.-J. Zhu, BLEU: A Method for Automatic Evaluation of Machine Translation, in: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, 2002: pp. 311–318.
[84]M.E. Peters, W. Ammar, C. Bhagavatula, R. Power, Semi-supervised sequence tagging with bidirectional language models, 2017.
[85]M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Deep contextualized word representations, 2018.
[86]A. Radford, J.W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, Learning Transferable Visual Models From Natural Language Supervision, (2021).
[87]A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, others, Improving language understanding by generative pre-training, in: OpenAI, 2018.
[88]A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, others, Language models are unsupervised multitask learners, OpenAI Blog. 1 (2019) 9.
[89]R.B. Randall, J. Antoni, Rolling element bearing diagnostics—A tutorial, Mechanical Systems and Signal Processing. 25 (2011) 485–520.
[90]F. Rekathati, The KBLab Blog: Introducing a Swedish Sentence Transformer, 2021.
[91]A. Rogers, On word analogies and negative results in NLP, 2019.
[92]A. Rogers, A. Drozd, B. Li, The (too many) problems of analogical reasoning with word vectors, in: Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (* SEM 2017), 2017: pp. 135–148.
[93]H. Rubenstein, J.B. Goodenough, Contextual Correlates of Synonymy, Commun. ACM. 8 (1965) 627–633.
[94]K. Saetia, S. Lukens, E. Pijcke, X. Hu, Data-driven Approach to Equipment Taxonomy Classification, Annual Conference of the PHM Society. (2019).
[95]S. Sarica, J. Luo, Stopwords in technical language processing, PLOS ONE . 16 (2021) e0254937.
[96]M.B. Sariyildiz, J. Perez, D. Larlus, Learning Visual Representations with Caption Annotations, in: A. Vedaldi, H. Bischof, T. Brox, J.-M. Frahm (Eds.), Computer Vision – ECCV 2020, n.d.
[97]N. Schluter, The Word Analogy Testing Caveat, in: NAACL, 2018.
[98]T. Schnabel, I. Labutov, D. Mimno, T. Joachims, Evaluation methods for unsupervised word embeddings, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Lisbon, Portugal, 2015: pp. 298–307.
[99]F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: A unified embedding for face recognition and clustering, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2015).
[100]R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, in: 2017 IEEE International Conference on Computer Vision (ICCV), 2017: pp. 618–626.
[101]T. Sexton, M. Brundage, Nestor: A Tool for Natural Language Annotation of Short Texts, Journal of Research of the National Institute of Standards and Technology. 124 (2019).
[102]T. Sexton, M.P. Brundage, M. Hoffman, K.C. Morris, Hybrid datafication of maintenance logs from AI-assisted human tags, 2017 IEEE International Conference on Big Data (Big Data). (2017) 1769–1777.
[103]T. Sexton, M.D. Fuge, Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining., Journal of Mechanical Design. 142 3 (2020).
[104]T. Sexton, M.R. Hodkiewicz, M.P. Brundage, Categorization Errors for Data Entry in Maintenance Work-Orders, Annual Conference of the PHM Society. (2019).
[105]T. Sexton, M.R. Hodkiewicz, M.P. Brundage, T. Smoker, Benchmarking for Keyword Extraction Methodologies in Maintenance Work Orders, Annual Conference of the PHM Society. (2018).
[106]M. Sharp, T. Sexton, M.P. Brundage, Toward Semi-autonomous Information - Extraction for Unstructured Maintenance Data in Root Cause Analysis, in: APMS, 2017.
[107]S. Shen, H. Lu, M. Sadoughi, C. Hu, V. Nemani, A. Thelen, K. Webster, M. Darr, J. Sidon, S. Kenny, A physics-informed deep learning approach for bearing fault detection, Engineering Applications of Artificial Intelligence. 103 (2021) 104295.
[108]V. Sobal, J. S, S. Jalagam, N. Carion, K. Cho, Y. LeCun, Joint Embedding Predictive Architectures Focus on Slow Features, arXiv, 2022.
[109]F. Stahlberg, B. Byrne, On NMT Search Errors and Model Errors: Cat Got Your Tongue?, 2019.
[110]S. Tang, S. Yuan, Y. Zhu, Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery, IEEE Access. 8 (2020) 9335–9346.
[111]M. Tong, B. Xu, S. Wang, Y. Cao, L. Hou, J. Li, J. Xie, Improving Event Detection via Open-domain Trigger Knowledge, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020: pp. 5887–5897.
[112]Y. Tsvetkov, M. Faruqui, W. Ling, G. Lample, C. Dyer, Evaluation of Word Vector Representations by Subspace Alignment, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Lisbon, Portugal, 2015: pp. 2049–2054.
[113]P.D. Turney, P. Pantel, From Frequency to Meaning: Vector Space Models of Semantics, Journal of Artificial Intelligence Research. 37 (2010) 141–188. [114]J.P. Usuga-Cadavid, B. Grabot, S. Lamouri, A. Fortin, Artificial Data Generation with Language Models for Imbalanced Classification in Maintenance, Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. (2021).
[115]A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, 2017.
[116]R. Vedantam, C.L. Zitnick, D. Parikh, CIDEr: Consensus-based Image Description Evaluation, 2015.
[117]A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, S. Bowman, SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019.
[118]A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, S.R. Bowman, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding, 2019.
[119]Z. Wang, X. Wang, X. Han, Y. Lin, L. Hou, Z. Liu, P. Li, J. Li, J. Zhou, CLEVE: Contrastive Pre-training for Event Extraction, in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021: pp. 6283–6297.
[120]Q. Wu, D. Teney, P. Wang, C. Shen, A. Dick, A. van den Hengel, Visual question answering: A survey of methods and datasets, Computer Vision and Image Understanding. 163 (2017) 21–40.
[121]J. Yan, C. Wu, F. Meng, J. Zhou, Rethinking the Evaluation of Neural Machine Translation, 2021.
[122]L. Yao, R. Huang, L. Hou, G. Lu, M. Niu, H. Xu, X. Liang, Z. Li, X. Jiang, C. Xu, FILIP: Fine-grained Interactive Language-Image Pre-Training, ArXiv. abs/2111.07783 (2021).
[123]Q. You, H. Jin, Z. Wang, C. Fang, J. Luo, Image Captioning with Semantic Attention, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: pp. 4651–4659.
[124]P. Zhang, X. Li, X. Hu, J. Yang, L. Zhang, L. Wang, Y. Choi, J. Gao, VinVL: Revisiting Visual Representations in Vision-Language Models, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2021) 5575–5584.
[125]T. Zhang, J. Chen, F. Li, K. Zhang, H. Lv, S. He, E. Xu, Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions, ISA Transactions. 119 (2022) 152–171.
[126]X. Zhang, T. Fujiwara, S.K. Chandrasegaran, M.P. Brundage, T. Sexton, A. Dima, K.-L. Ma, A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data, 2021 IEEE 14th Pacific Visualization Symposium (PacificVis). (2021) 196–205.
[127]Y. Zhang, H. Jiang, Y. Miura, C.D. Manning, C.P. Langlotz, Contrastive Learning of Medical Visual Representations from Paired Images and Text, arXiv, 2020.
[128]Z. Zhao, Q. Zhang, X. Yu, C. Sun, S. Wang, R. Yan, X. Chen, Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study, IEEE Transactions on Instrumentation and Measurement. 70 (2021) 1–28.
[129]Z.-H. Zhou, A brief introduction to weakly supervised learning, National Science Review. 5 (2017) 44–53.
Technical Research Papers