Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry



Published Oct 25, 2022
Karl Löwenmark Cees Taal Stephan Schnabel Marcus Liwicki Fredrik Sandin


In the process industry, condition monitoring systems with automated fault diagnosis methods assist human 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 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 digitised 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 opportunity to develop technical language supervision (TLS) solutions 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 generalisation. We surveyed the literature and identify a considerable 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.

Abstract 107 | PDF Downloads 101



Intelligent Fault Diagnosis, Natural Language Supervision, Technical Language Processing, Condition Monitoring, Technical Language Supervision, Natural Language Processing

Aghdam, H. H., Gonzalez-Garcia, A., van de Weijer, J., & López, A. M. (2019). Active learning for deep detection neural networks.
An, Z., Li, S., Xin, Y., Xu, K., & Ma, H. (2019). An intelligent fault diagnosis framework dealing with arbitrary length inputs under different working conditions. Measurement Science and Technology, 30(12).
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). Bottom-up and top-down attention for image captioning and visual question answering.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015, December). Vqa: Visual question answering. In Proceedings of the ieee international conference on computer vision (iccv).
Babu, G., Zhao, P., & Li, X.-L. (2016). Deep convolutional neural network based regression approach for estimation of remaining useful life. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9642, 214-228.
Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150-172.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., . . . Amodei, D. (2020a). Language models are few-shot learners.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., . . . Amodei, D. (2020b). Language models are few-shot learners.
Brundage, M. P., Sexton, T., Hodkiewicz, M., Dima, A., & Lukens, S. (2021). Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters, 27, 42-46.
Brundage, M. P., Sharp, M., & Pavel, R. (2021, Jun). Qualifying evaluations from human operators: Integrating sensor data with natural language logs. PHM Society European Conference.
Cadavid, J. P. U., Grabot, B., Lamouri, S., Pellerin, R., & Fortin, A. (2020). Valuing free-form text data from maintenance logs through transfer learning with camembert. Enterprise Information Systems, 0(0), 1-29.
Cahill, J. (2021). Improving subsurface models to reduce drilling uncertainty.
Cao, P., Zhang, S., & Tang, J. (2018a). Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning. IEEE Access, 6, 26241-26253.
Cao, P., Zhang, S., & Tang, J. (2018b). Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning. IEEE Access, 6, 26241-26253.
Carden, E. P., & Fanning, P. (2004). Vibration based condition monitoring: A review. Structural Health Monitoring, 3(4), 355-377.
Case western reserve university bearing data center website. (n.d.). (Accessed: 2020-01-30)
Cerrada, M., S´anchez, R.-V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J. V., & V´asquez, R. E. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169 - 196.
Chen, F., Zhang, D., Han, M., Chen, X., Shi, J., Xu, S., & Xu, B. (2022a). Vlp: A survey on vision-language pre-training. arXiv.
Chen, F., Zhang, D., Han, M., Chen, X., Shi, J., Xu, S., & Xu, B. (2022b). Vlp: A survey on vision-language pre-training. arXiv.
Chen, X., Zhang, B., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale cnn and lstm model. Journal of Intelligent Manufacturing, 32(4), 971-987.
Chen, Z., Gryllias, K., & Li, W. (2020). Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Transactions on Industrial Informatics, 16(1), 339-349.
Chung Fu, T. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164-181.
Condition based maintenance fault database for testing of diagnostic and prognostics algorithms. (n.d.). (Accessed: 2021-09-30)
del Campo, S. M., & Sandin, F. (2017). Online feature learning for condition monitoring of rotating machinery. Engineering Applications of Artificial Intelligence, 64, 187 - 196.
Desai, K., & Johnson, J. (2020). Virtex: Learning visual representations from textual annotations.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding.
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1), 31-71.
Dima, A., Lukens, S., Hodkiewicz, M., Sexton, T., & Brundage, M. P. (2021). Adapting natural language processing for technical text. Applied AI Letters, 2(3), e33.
Ding, S., Qu, S., Xi, Y., & Wan, S. (2020). Stimulus-driven and concept-driven analysis for image caption generation. Neurocomputing, 398, 520-530.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., . . . Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale.
Ekström, K., & Sandin, F. (2020). Fault severity estimation using weak supervision with language based labels and condition monitoring data.
Elhoseiny, M., Saleh, B., & Elgammal, A. (2013). Write a classifier: Zero-shot learning using purely textual descriptions. In 2013 ieee international conference on computer vision (p. 2584-2591).
Emerson. (2021). Featured technologies/machine learning.
Feng, L., & Zhao, C. (2021). Fault description based attribute transfer for zero-sample industrial fault diagnosis. IEEE Transactions on Industrial Informatics, 17(3), 1852-1862.
Fink, O., Wang, Q., Svens´en, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92, 103678.
Gage, P. (1994). A new algorithm for data compression. The C Users Journal archive, 12, 23-38.
Gao, Y., Gao, L., Li, X., & Zheng, Y. (2020). A zero-shot learning method for fault diagnosis under unknown working loads. Journal of Intelligent Manufacturing, 31(4), 899-909.
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2019). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316-7325.
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98-109.
Haidong, S., Hongkai, J., Xingqiu, L., & Shuaipeng, W. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 140, 1-14.
Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., . . . Tao, D. (2021). A survey on visual transformer.
Han, T., Liu, C., Yang, W., & Jiang, D. (2019). A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 165, 474-487.
Han, T., Liu, C., Yang, W., & Jiang, D. (2020). Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA Transactions, 97, 269 - 281.
Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., & Ré, C. (2018). Training classifiers with natural language explanations.
He, S., Liao, W., Tavakoli, H. R., Yang, M., Rosenhahn, B., & Pugeault, N. (2020). Image captioning through image transformer.
He, Z., Shao, H., Zhong, X., & Zhao, X. (2020). Ensemble transfer cnns driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions. Knowledge-Based Systems, 207.
Helbing, G., & Ritter, M. (2018). Deep learning for fault detection in wind turbines. Renewable and Sustainable Energy Reviews, 98, 189 - 198.
Hoang, D.-T., & Kang, H.-J. (2019). A survey on deep learning based bearing fault diagnosis. Neurocomputing, 335, 327-335.
Hodkiewicz, M. R., Batsioudis, Z., Radomiljac, T., & Ho, M. T. (2017). Why autonomous assets are good for reliability – the impact of ‘operator-related component’ failures on heavy mobile equipment reliability.
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., & Weld, D. S. (2011, June). Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 541–550). Portland, Oregon, USA: Association for Computational Linguistics.
Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483 - 1510.
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., . . . Duerig, T. (2021). Scaling up visual and vision-language representation learning with noisy text supervision.
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72-73, 303-315.
Jia, F., Lei, Y., Lu, N., & Xing, S. (2018). Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mechanical Systems and Signal Processing, 110, 349-367.
Jian, C., Yang, K., & Ao, Y. (2021). Industrial fault diagnosis based on active learning and semi-supervised learning using small training set. Engineering Applications of Artificial Intelligence, 104, 104365.
Jiang, G., Xie, P., He, H., & Yan, J. (2018). Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/ASME Transactions on Mechatronics, 23(1), 89-100.
Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265.
Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2006, Jul 01). System health monitoring and prognostics — a review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, 28(9), 1012-1024.
Labutov, I., Yang, B., & Mitchell, T. (2019). Learning to learn semantic parsers from natural language supervision.
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137-3147.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical Systems and Signal Processing, 104, 799-834.
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.
Li, C., Zhang, S., Qin, Y., & Estupinan, E. (2020). A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 407, 121 - 135.
Li, J.,Wong, Y., Zhao, Q., & Kankanhalli, M. (2019). Learning to learn from noisy labeled data. In (Vol. 2019-June, p. 5046-5054).
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering and System Safety, 172, 1-11.
Li, X., Li, X., & Ma, H. (2020). Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery. Mechanical Systems and Signal Processing, 143, 106825.
Li, X., Zhang, W., Ding, Q., & Li, X. (2020). Diagnosing rotating machines with weakly supervised data using deep transfer learning. IEEE Transactions on Industrial Informatics, 16(3), 1688-1697.
Li, X., Zhang, W., Ding, Q., & Li, X. (2020). Diagnosing rotating machines with weakly supervised data using deep transfer learning. IEEE Transactions on Industrial Informatics, 16(3), 1688-1697.
Li, X., Zhang, W., Xu, N.-X., & Ding, Q. (2020). Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places. IEEE Transactions on Industrial Electronics, 67(8), 6785-6794.
Li, Y., Lin, T., Yi, K., Bear, D. M., Yamins, D. L. K., Wu, J., . . . Torralba, A. (2020). Visual grounding of learned physical models.
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., . . . Doll´ar, P. (2015). Microsoft coco: Common objects in context.
Liu, H., Liu, C., & Huang, Y. (2011). Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mechanical Systems and Signal Processing, 25(2), 558 - 574.
Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018). Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, 77, 167-178.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33-47.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., . . . Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.
Lochter, J. V., Silva, R. M., & Almeida, T. A. (2020). Deep learning models for representing out-of-vocabulary words.
Lowenmark, K., Taal, C., Nivre, J., Liwicki, M., & Sandin, F. (2022). Processing of condition monitoring annotations with bert and technical language substitution: A case study. Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022, 306-314.
Lu, C.,Wang, Z.-Y., Qin, W.-L., & Ma, J. (2017). Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 130, 377-388.
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.
Lu, X., Wang, B., Zheng, X., & Li, X. (2018). Exploring models and data for remote sensing image caption generation. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2183-2195.
Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019). The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision.
Microsoft. (2020). Turing-nlg: A 17-biliion paramater language model by microsoft.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality.
Monitron, A. (n.d.). Detect abnormal machine behavior and enable predictive maintenance.
Murty, S., Koh, P. W., & Liang, P. (2020). Expbert: Representation engineering with natural language explanations.
Nan, C., Khan, F., & Iqbal, M. T. (2008). Real-time fault diagnosis using knowledge-based expert system. Process Safety and Environmental Protection, 86(1), 55-71.
Nandyala, A., Lukens, S., Rathod, S., & Agarwal. (2021, Jun). Evaluating word representations in a technical language processing pipeline. PHM Society European Conference.
Nasa prognostic data repository. (n.d.). (Accessed: 2021-01-30)
Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2018). Liftingnet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification. IEEE Transactions on Industrial Electronics, 65(6), 4973-4982.
Papyan, V., Romano, Y., Sulam, J., & Elad, M. (2018). Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks. IEEE Signal Processing Magazine, 35(4), 72-89.
PdM. (2021). Pdm services vibration analysis monitoring.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. ProcessIT. (2018). european roadmap for process industrial automation. second version. , 3(3).
Qiao, M., Yan, S., Tang, X., & Xu, C. (2020). Deep convolutional and lstm recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access, 8, 66257-66269.
Qin, S. (2012). Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 36(2), 220-234.
Radford, A. (2018a). Improving language understanding by generative pre-training.
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., . . . Sutskever, I. (2021a). Learning transferable visual models from natural language supervision.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019b). Language models are unsupervised multitask learners.
Rai, A., & Upadhyay, S. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289-306.
Ramanathan, V., Liang, P., & Fei-Fei, L. (2013). Video event understanding using natural language descriptions. In 2013 ieee international conference on computer vision (p. 905-912).
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., . . . Sutskever, I. (2021). Zero-shot text-to-image generation.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing, 25(2), 485 - 520.
Razavi, A., van den Oord, A., & Vinyals, O. (2019). Generating diverse high-fidelity images with vq-vae-2.
Razavi-Far, R., Hallaji, E., Farajzadeh-Zanjani, M., & Saif, M. (2019). A semi-supervised diagnostic framework based on the surface estimation of faulty distributions. IEEE Transactions on Industrial Informatics, 15(3), 1277-1286.
Reimers, N., & Gurevych, I. (2019, November). Sentence-BERT: Sentence embeddings using Siamese BERTnetworks. In EMNLP-IJCNLP 2019 (pp. 3982–3992). Hong Kong, China: Association for Computational Linguistics. (First published as pre-print on arXiv.)
Rekathati, F. (2021). The KBLab blog: Introducing a Swedish sentence transformer.
Ren, Z., Zhu, Y., Yan, K., Chen, K., Kang, W., Yue, Y., & Gao, D. (2020). A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis. Mechanical Systems and Signal Processing, 138.
Sarica, S., & Luo, J. (2021, Aug). Stopwords in technical language processing. PLOS ONE, 16(8), e0254937.
Sariyildiz, M. B., Perez, J., & Larlus, D. (2020). Learning visual representations with caption annotations.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015, Jun). Facenet: A unified embedding for face recognition and clustering. CVPR 2015.
Schuster, M., & Nakajima, K. (2012). Japanese and korean voice search. In ICASSP 2012 (p. 5149-5152).
Schwendemann, S., Amjad, Z., & Sikora, A. (2021). Bearing fault diagnosis with intermediate domain based layered maximum mean discrepancy: A new transfer learning approach. Engineering Applications of Artificial Intelligence, 105, 104415.
Sennrich, R., Haddow, B., & Birch, A. (2015). Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909.
Sexton, T., Brundage, M., Hodkiewicz, M., & Smoker, T. (2018, 2018-09-24). Benchmarking for keyword extraction methodologies in maintenance work orders. 2018 Annual Conference of the Prognostics and Health Management Society, Philadelphia, PA.
Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2021). Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images. IEEE Transactions on Industrial Informatics, 17(5), 3488-3496.
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446-2455.
Sharma, V., & Parey, A. (2016). A review of gear fault diagnosis using various condition indicators. In (Vol. 144, p. 253-263).
Sharp, M., Brundage, M., Sexton, T., & Madhusudanan, F. (2021, 2021-04-22 04:04:00). Discovering critical KPI factors from natural language in maintenance work orders, 3(3).
Shin, J.-H., & Jun, H.-B. (2015). On condition based maintenance policy. Journal of Computational Design and Engineering, 2(2), 119 - 127.
Simon, J. (n.d.). Amazon monitron, a simple and costeffective service enabling predictive maintenance. SKF. (n.d.). Skf enlight ai.
SKF. (2022). Skf @ptitude observer user manual.
SKF, A., & Kommunikation, S. (2020, March). Skf annual report 2020.
Smith, W., & Randall, R. (2015). Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mechanical Systems and Signal Processing, 64-65, 100-131.
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy, 133, 620-635.
Stief, A., Ottewill, J., Baranowski, J., & Orkisz, M. (2019). A pca and two-stage bayesian sensor fusion approach for diagnosing electrical and mechanical faults in induction motors. IEEE Transactions on Industrial Electronics, 66(12), 9510-9520.
Tanaka, D., Ikami, D., Yamasaki, T., & Aizawa, K. (2018). Joint optimization framework for learning with noisy labels. In (p. 5552-5560).
Tian, Y., Krishnan, D., & Isola, P. (2020). Contrastive multiview coding.
van den Oord, A., Vinyals, O., & Kavukcuoglu, K. (2018). Neural discrete representation learning.
van Engelen, J. E., & Hoos, H. H. (2020, Feb 01). A survey on semi-supervised learning. Machine Learning, 109(2), 373-440.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need.
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., . . . Bowman, S. R. (2020). Superglue: A stickier benchmark for general-purpose language understanding systems.
Wang, D., Tsui, K.-L., & Miao, Q. (2017). Prognostics and health management: A review of vibration based bearing and gear health indicators. IEEE Access, 6, 665-676.
Wang, Q., Michau, G., & Fink, O. (2019). Domain adaptive transfer learning for fault diagnosis. In (p. 279-285).
Wang, Y., Yao, Q., Kwok, J., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning.
Wang, Z., Yu, A. W., Firat, O., & Cao, Y. (2021). Towards zero-label language learning.
Wang, Z., Yu, J., Yu, A. W., Dai, Z., Tsvetkov, Y., & Cao, Y. (2021). Simvlm: Simple visual language model pretraining with weak supervision. arXiv.
Wen, L., Gao, L., & Li, X. (2019). A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 136-144.
Wen, L., Li, X., & Gao, L. (2020). A transfer convolutional neural network for fault diagnosis based on resnet-50. Neural Computing and Applications, 32(10), 6111-6124.
Williams, E. C., Gopalan, N., Rhee, M., & Tellex, S. (2018). Learning to parse natural language to grounded reward functions with weak supervision. In 2018 ieee international conference on robotics and automation (icra) (p. 4430-4436).
Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., . . . Vajda, P. (2020). Visual transformers: Token-based image representation and processing for computer vision.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., . . . Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. ArXiv, abs/1609.08144.
Xu, G., Liu, M., Jiang, Z., Shen, W., & Huang, C. (2020). Online fault diagnosis method based on transfer convolutional neural networks. IEEE Transactions on Instrumentation and Measurement, 69(2), 509-520.
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019). A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access, 7, 19990-19999.
Yan, X., She, D., Xu, Y., & Jia, M. (2021). Deep regularized variational autoencoder for intelligent fault diagnosis of rotor bearing system within entire life-cycle process. Knowledge-Based Systems, 226, 107142.
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 122, 692 - 706.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2020). Xlnet: Generalized autoregressive pretraining for language understanding.
Yao, L., Huang, R., Hou, L., Lu, G., Niu, M., Xu, H., . . . Xu, C. (2021). Filip: Fine-grained interactive language-image pre-training.
Yiakopoulos, C., Gryllias, K., & Antoniadis, I. (2011). Rolling element bearing fault detection in industrial environments based on a k-means clustering approach. Expert Systems with Applications, 38(3), 2888 - 2911.
Yin, S., Ding, S., Xie, X., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418-6428.
Yu, K., Fu, Q., Ma, H., Lin, T., & Li, X. (2021, 07). Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis. Structural Health Monitoring, 20.
Yu, K., Lin, T. R., Ma, H., Li, X., & Li, X. (2021). A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning. Mechanical Systems and Signal Processing, 146, 107043.
Zakir Hossain, M., Sohel, F., Shiratuddin, M., & Laga, H. (2019). A comprehensive survey of deep learning for image captioning. ACM Computing Surveys, 51(6).
Zeng, D., Liu, K., Chen, Y., & Zhao, J. (2015, September). Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 1753–1762). Lisbon, Portugal: Association for Computational Linguistics.
Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4l: Self-supervised semi-supervised learning.
Zhang, A., Li, S., Cui, Y., Yang, W., Dong, R., & Hu, J. (2019). Limited data rolling bearing fault diagnosis with few-shot learning. IEEE Access, 7, 110895-110904.
Zhang, A., Wang, H., Li, S., Cui, Y., Liu, Z., Yang, G., & Hu, J. (2018). Transfer learning with deep recurrent neural networks for remaining useful life estimation. Applied Sciences (Switzerland), 8(12).
Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018). A data-driven design for fault detection of wind turbines using random forests and xgboost. IEEE Access, 6, 21020-21031.
Zhang, H., Zhang, Q., Liu, J., & Guo, H. (2018). Fault detection and repairing for intelligent connected vehicles based on dynamic bayesian network model. IEEE Internet of Things Journal, 5(4), 2431-2440.
Zhang, Q., Lu, J., & Jin, Y. (2021, Feb 01). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439-457.
Zhang, S., Ye, F., Wang, B., & Habetler, T. G. (2019). Semisupervised learning of bearing anomaly detection via deep variational autoencoders.
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 8, 29857-29881.
Zhang, T., Chen, J., Li, F., Zhang, K., Lv, H., He, S., & Xu, E. (2021). Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA Transactions.
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D., & Langlotz, C. P. (2020). Contrastive learning of medical visual representations from paired images and text.
Zhang, Z., Wu, Q., Wang, Y., & Chen, F. (2019). High-quality image captioning with fine-grained and semantic-guided visual attention. IEEE Transactions on Multimedia, 21(7), 1681-1693.
Zhao, K., Jiang, H.,Wu, Z., & Lu, T. (2020). A novel transfer learning fault diagnosis method based on manifold embedded distribution alignment with a little labeled data. Journal of Intelligent Manufacturing.
Zhong, S.-S., Fu, S., & Lin, L. (2019). A novel gas turbine fault diagnosis method based on transfer learning with cnn. Measurement: Journal of the International Measurement Confederation, 137, 435-453.
Zhou, D., He, J., Yang, H., & Fan, W. (2018). Sparc: Self-paced network representation for few-shot rare category characterization. In (p. 2807-2816).
Zhou, Z.-H. (2017, 08). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44-53.
Technical Papers