LIST_OF_CITATIONS.md

As far as we know, the 3W Dataset was useful and cited by the works listed below. If you know any other paper, final graduation project, master's degree dissertation or doctoral thesis that cites the 3W Dataset, we will be grateful if you let us know by commenting this discussion. If you use any resource published in this repository, we ask that it be properly cited in your work. Click on the Cite this repository link on this repository landing page to access different citation formats supported by the GitHub citation feature.

1. R.E.V. Vargas, C.J. Munaro, P.M. Ciarelli. A methodology for generating datasets for development of anomaly detectors in oil wells based on Artificial Intelligence techniques. I Congresso Brasileiro em Engenharia de Sistemas em Processos. 2019. https://www.ufrgs.br/psebr/wp-content/uploads/2019/04/Abstract_A019_Vargas.pdf.

2. R.E.V. Vargas. Base de dados e benchmarks para prognóstico de anomalias em sistemas de elevação de petróleo. Universidade Federal do Espírito Santo. Doctoral thesis. 2019. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_ricardo_vargas.pdf.

3. Y. Li, T. Ge, C. Chen. Data Stream Event Prediction Based on Timing Knowledge and State Transitions. Very Large Data Base Endowment. 2020. http://www.vldb.org/pvldb/vol13/p1779-li.pdf.

4. T. Lu, W. Xia, X. Zou, Q. Xia. Adaptively Compressing IoT Data on the Resource-constrained Edge. 3rd {USENIX} Workshop on Hot Topics in Edge Computing. 2020. https://www.usenix.org/system/files/hotedge20_paper_lu.pdf.

5. W.F. Junior, R.E.V. Vargas, K.S. Komati, K.A.S. Gazolli. Detecção de anomalias em poços produtores de petróleo usando aprendizado de máquina. XXIII Congresso Brasileiro de Automática. 2020. https://www.sba.org.br/open_journal_systems/index.php/cba/article/download/1405/1005.

6. J. Liu, J. Gu, H. Li, K.H. Carlson. Machine learning and transport simulations for groundwater anomaly detection. Journal of Computational and Applied Mathematics. 2020. https://doi.org/10.1016/j.cam.2020.112982.

7. E.S.P. Sobrinho, F.L. Oliveira, J.L.R. Anjos, C. Gonçalves, M.V.D. Ferreira, L.G.O. Lopes, W.W.M. Lira, J.P.N. Araújo, T.B. Silva, L.P. Gouveia. Uma ferramenta para detectar anomalias de produção utilizando aprendizagem profunda e árvore de decisão. Rio Oil & Gas Expo and Conference. 2020. https://icongresso.ibp.itarget.com.br/arquivos/trabalhos_completos/ibp/3/final.IBP0938_20_27112020_085551.pdf.

8. I.M.N. Oliveira. Técnicas de inferência e previsão de dados como suporte à análise de integridade de revestimentos. Universidade Federal de Alagoas. Master's degree dissertation. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_igor_oliveira.pdf.

9. L. Müller, M.R. Martins. Proposition of Reliability-based Methodology for Well Integrity Management During Operational Phase. 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. 2020. https://doi.org/10.3850%2F978-981-14-8593-0_3682-cd.

10. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Detecção de falhas com Stacked Autoencoders e técnicas de reconhecimento de padrões em poços de petróleo operados por gas lift. XXIII Congresso Brasileiro de Automática. 2020. https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1462/1300.

11. M.J.R. Santos, A.O.S. Castro, G.S. Ferreira, A.L. D'Almeida, G.B.A. Lima, F.R. Leta, C.B.C. Lima, L.C. Maia. Utilização de modelos estatísticos para detecção precoce de falhas em poços de petróleo offshore. Rio Oil & Gas Expo and Conference. 2020. https://biblioteca.ibp.org.br/scripts/bnmapi.exe?router=upload/33989.

12. R.L. Rosa. Classificação de eventos indesejaveis na produção de petróleo offshore com aplicação de técnicas de inteligência artificial. Universidade Federal Fluminense. Final Graduation Project. 2020. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_renato_rosa.pdf.

13. M.J.R. Santos. Detecção de problemas de garantia de escoamento a partir da utilização de ferramentas de aprendizado de máquina. Universidade Federal Fluminense. Master's degree dissertation. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_mayara_santos.pdf.

14. R. Karl, J. Takeshita, N. Koirla, Taeho Jung. Cryptonite: A Framework for Flexible Time-Series Secure Aggregation with Online Fault Tolerance. Cryptology ePrint Archive. 2020. https://eprint.iacr.org/2020/1561.

15. C. Brønstad. Data-driven detection and identification of undesirable events in subsea oil wells. University of South-Eastern Norway. Master's degree dissertation. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_chrisander_bronstad.pdf.

16. M.A. Marins, B.D. Barros, I.H. Santos, D.C. Barrionuevo, R.E.V. Vargas, T.M. Prego, A.A. Lima, M.L.R. Campos, E.A.B. Silva, S.L. Netto. Fault detection and classification in oil wells and production/service lines using random forest. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2020.107879.

17. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Detecção de anomalias em poços de petróleo surgentes com Stacked Autoencoders. Simpósio Brasileiro de Automação Inteligente. 2021. https://doi.org/10.20906/sbai.v1i1.2856.

18. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Fault detection with Stacked Autoencoders and pattern recognition techniques in gas lift operated oil wells. XLII Ibero-Latin-American Congress on Computational Methods in Engineering. 2021. No link yet.

19. R.S.F. Nascimento. Detecção de anomalias em poços de produção de petróleo offshore com a utilização de autoencoders e técnicas de reconhecimento de padrões. Universidade Federal de Lavras. Master's degree dissertation. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_rodrigo_nascimento.pdf.

20. T. Hafeez, L. Xu, G. Mcardle. Edge Intelligence for Data Handling and Predictive Maintenance in IIOT. IEEE Access. 2021. https://ieeexplore.ieee.org/document/9387301.

21. A.S. Vargas, R. Werneck, R. Moura, P.M. Júnior, R. Prates, M. Castro, M. Gonçalves, M. Hossain, M. Zampieri, A. Ferreira, A. Davólio, B. Hamann, D.J. Schiozer, A. Rocha. A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2021.108988.

22. Y. Li, T. Ge. Imminence Monitoring of Critical Events: A Representation Learning Approach. International Conference on Management of Data. 2021. https://doi.org/10.1145/3448016.3452804.

23. B.G. Carvalho, R.E.V. Vargas, R.M. Salgado, C.J. Munaro, F.M. Varejão. Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers. 30th International Symposium on Industrial Electronics. 2021. https://doi.org/10.1109/ISIE45552.2021.9576310.

24. B.G. Carvalho, R.E.V. Vargas, R.M. Salgado, C.J. Munaro, F.M. Varejão. Hyperparameter Tuning and Feature Selection for Improving Flow Instability Detection in Offshore Oil Wells. IEEE 19th International Conference on Industrial Informatics. 2021. https://doi.org/10.1109/INDIN45523.2021.9557415.

25. B.G. Carvalho. Evaluating machine learning techniques for detection of flow instability events in offshore oil wells. Universidade Federal do Espírito Santo. Master's degree dissertation. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_bruno_carvalho.pdf.

26. E.M. Turan, J. Jäschke. Classification of undesirable events in oil well operation. 23rd International Conference on Process Control. 2021. https://doi.org/10.1109/PC52310.2021.9447527.

27. I.S. Figueirêdo, T.F. Carvalho, W.J.D Silva, L.L.N. Guarieiro, E.G.S. Nascimento. Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning. OTC Offshore Technology Conference. 2021. https://doi.org/10.4043/31297-MS.

28. R. Karl, J. Takeshita, T. Jung. Cryptonite: A Framework for Flexible Time-Series Secure Aggregation with Non-interactive Fault Recovery. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering. 2021. http://dx.doi.org/10.1007/978-3-030-90019-9_16.

29. A.O.S. Castro, M.J.R. Santos, F.R. Leta, C.B.C. Lima, G.B.A. Lima. Unsupervised Methods to Classify Real Data from Offshore Wells. American Journal of Operations Research. 2021. https://doi.org/10.4236/ajor.2021.115014.

30. M.J.R. Santos, A.O.S. Castro, F.R. Leta, J.F.M. Araujo, G.S. Ferreira, R.A. Santos, C.B.C. Lima, G.B.A. Lima. Statistical analysis of offshore production sensors for failure detection applications. Brazilian Journal of Development. 2021. https://doi.org/10.34117/bjdv7n8-681.

31. M.J.R. Santos, M.P.C. Fonseca, F.R. Leta, J.F.M. Araujo, G.S. Ferreira, G.B.A. Lima, C.B.C. Lima, L.C. Maia. Classificação de problemas de garantia de escoamento pormeio de algoritmos de machine learning. Series of the Brazilian Society of Computational and Applied Mathematics. 2021. https://proceedings.sbmac.org.br/sbmac/issue/view/11.

32. L.E.G. Vignoli. Análise Comparativa de Métodos para Detecção de Eventos em Séries Temporais. Centro Federal de Educação Tecnológica Celso Suckow da Fonseca. Master's degree dissertation. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_luciana_vignoli.pdf.

33. L. Müller. Proposição de metodologia baseada em confiabilidade para gerenciamento da integridade de poços em produção. Universidade de São Paulo. Master's degree dissertation. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_luiz_muller.pdf.

34. L.G.O. Lopes, T.M.A. Vieira, W.W.M. Lira. Automatic evaluation of scientific abstracts through natural language processing. arXiv. 2021. https://doi.org/10.48550/arXiv.2112.01842.

35. I.S. Figueirêdo, T.F. Carvalho, W.D. Silva, L.L.N. Guarieiro, A.A.B. Santos, L.S.M. Filho, R.E.V. Vargas, E.G.S. Nascimento. Unsupervised Machine Learning for Anomaly Detection in Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform. Journal of Systemics, Cybernetics and Informatics. 2021. https://www.iiisci.org/journal/PDV/sci/pdfs/ZA422HO21.pdf.

36. F.M. Varejão. Diagnóstico Inteligente de Falhas em Equipamentos Industriais. Revista de Sistemas de Informação da FSMA. 2021. http://www.fsma.edu.br/si/edicao28/FSMA_SI_2021_2_04_Varejao_Final.pdf.

37. S.V. Tsyplenkov, E.D. Agafonov. The concept of an integrated system of energy efficiency control of artifical oil lift. Power Engineering Research Equipment Technology. 2021. http://dx.doi.org/10.30724/1998-9903-2021-23-4-180-196.

38. C. Brønstad, S.L. Netto, A.L.L. Ramos. Data-driven Detection and Identification of Undesirable Events in Subsea Oil Wells. The Twelfth International Conference on Sensor Device Technologies and Applications. 2021. https://www.thinkmind.org/index.php?view=article&articleid=sensordevices_2021_1_10_28039.

39. W.F. Junior. Comparação de classificadores para detecção de anomalias em poços produtores de petróleo. Instituto Federal do Espírito Santo. Master's degree dissertation. 2022. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_wander_junior.pdf.

40. E.G.S. Nascimento, I.S. Figueirêdo, L.L.N. Guarieiro. A Novel Self Deep Learning Semi-Supervised Approach to Classify Unlabeled Multivariate Time Series Data. GPU Technology Conference Digital Spring. 2022. https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41405.

41. A.L. D’Almeida, N.C.R. Bergiante, G.S. Ferreira, F.R. Leta, C.B.C. Lima, G.B.A. Lima. Digital transformation: a review on artificial intelligence techniques in drilling and production applications. The International Journal of Advanced Manufacturing Technology. 2022. https://doi.org/10.1007/s00170-021-08631-w.

42. A.P.F. Machado, R.E.V. Vargas, P.M. Ciarelli, C.J. Munaro. Improving performance of one-class classifiers applied to anomaly detection in oil wells. Journal of Petroleum Science and Engineering. 2022. https://doi.org/10.1016/j.petrol.2022.110983.

43. N. Aslam, I.U. Khan, A. Alansari, M. Alrammah, A. Alghwairy, R. Alqahtani, R. Alqahtani, M. Almushikes, M.A. Hashim. Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells. Applied Computational Intelligence and Soft Computing. 2022. https://doi.org/10.1155/2022/1558381.

44. F. Gatta, F. Giampaolo, D. Chiaro, F. Piccialli. Predictive maintenance for offshore oil wells by means of deep learning features extraction. Expert Systems. 2022. https://doi.org/10.1111/exsy.13128.

45. A. Melo, M.M. Câmara, N. Clavijo, J.C. Pinto. Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis. Computers & Chemical Engineering. 2022. https://doi.org/10.1016/j.compchemeng.2022.107964.

46. D. Leite, A. Martins, D. Rativa, J.F.L. de Oliveira, A.M.A. Maciel. An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis. Sensors. 2022. https://doi.org/10.3390%2Fs22166138.

47. M.C.K. de Oliveira, R.L.A. Pinto, J.N.E. Carneiro. A digital transformation journey in flow assurance. T&B Petroleum magazine. 2022. https://tbpetroleum.com.br/revistas/2022/41.

48. G.G. Momm. Detecção de anomalias em sensores de poços submarinos com uso de redes neurais artificiais. Universidade de São Paulo. Specialization Monograph. 2022. https://github.com/petrobras/3W/raw/main/docs/specialization_monograph_gustavo_momm.pdf.

49. L.H.S. Mello, T.O. Santos, F.M. Varejão, M.P. Ribeiro, A.L. Rodrigues. Ensemble of metric learners for improving electrical submersible pump fault diagnosis. Journal of Petroleum Science and Engineering. 2022. https://doi.org/10.1016/j.petrol.2022.110875.

50. Y.F. Yeung, A.P. Ajuwape, F. Tahiry, M. Furokawa, T. Hirano, K.Y. Toumi. RoSA: A Mechatronically Synthesized Dataset for Rotodynamic System Anomaly Detection. IEEE International Conference on Intelligent Robots and Systems. 2022. https://doi.org/10.1109/IROS47612.2022.9982146.

51. M.G. Proença. Modelos de aprendizado de máquina aplicados à detecção de anomalias em poços produtores de petróleo. Universidade Federal do Paraná. Final Graduation Project. 2022. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_martim_proenca.pdf.

52. P.E. Coutinho, L.H.M. Silveira, M. Cataldi, F.R. Leta, A.O.S. Castro, C.B.C. Lima, G.B.A. Lima. Wavelet Transform Processing in Detecting Failures in Offshore Well Production. Latin American Journal of Energy Research. 2022. https://doi.org/10.21712/lajer.2022.v9.n1.p1-11.

53. R. Olsson, C. Tran, L. Magnusson. Automatic Synthesis of Neurons for Recurrent Neural Nets. arXiv. 2022. https://doi.org/10.48550/arXiv.2207.03577.

54. S. Casolo. Severe slugging flow identification from topological indicators. Digital Chemical Engineering. 2022. https://doi.org/10.1016/j.dche.2022.100045.

55. A. Harrouz, H. Toubakh, M.R. Kafi, S.M. Moamar, S. Hajer. Dynamic Linear Regression Models for Down Hole Safety Valve Remaining Useful Life Prediction. Annual conference of the prognostics and health management society 2022. 2022. https://doi.org/10.36001/phmconf.2022.v14i1.3227.

56. Y.S.A. ElWahab, M.M. Nasr, F.K.A. Sheref. An intelligent oil accident predicting and classifying system using deep learning techniques. Indonesian Journal of Electrical Engineering and Computer Science. 2023. https://doi.org/10.11591/ijeecs.v29.i1.pp460-471.

57. I.S. Figueirêdo. Uma nova abordagem de inteligência artificial baseada em autoaprendizagem profunda para manutenção preditiva em um ambiente de produção de petróleo e gás offshore. Centro Universitário Senai Cimatec. Doctoral thesis. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_ilan_figueiredo.pdf.

58. L.V. Magnusson, J.R. Olsson, C. Tran. Recurrent Neural Networks for Oil Well Event Prediction. IEEE Intelligent Systems. 2023. https://doi.org/10.1109/MIS.2023.3252446.

59. A.V.S. Alves. Sensores virtuais baseados em aprendizado de máquina para poços de petróleo. Universidade de Brasília. Final Graduation Project. 2023. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_arthur_alves.pdf.

60. R. Schena. A methodology for synthetic generation of failure data for data-driven prognostics and health management (PHM) modeling for digital twins. Universidade Federal do Rio Grande do Sul. Master's degree dissertation. 2023. https://lume.ufrgs.br/handle/10183/267589.

61. R. Salles, J. Lima, R. Coutinho, E. Pacitti, F. Masseglia, R. Akbarinia, C. Chen, J. Garibaldi, F. Porto, E. Ogasawara. SoftED: Metrics for Soft Evaluation of Time Series Event Detection. arXiv. 2023. https://doi.org/10.48550/arXiv.2304.00439.

62. A.J.M. Junior. Integração humano-máquina para o monitoramento de processos industriais baseado em dados. Universidade Federal do Rio de Janeiro. Doctoral thesis. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_afranio_junior.pdf.

63. B. Chen, X. Zeng, W. Zhang, L. Fan, S. Cao, J. Zhou. Knowledge sharing-based multi-block federated learning for few-shot oil layer identification. Energy. 2023. https://doi.org/10.1016/j.energy.2023.128406.

64. I. Yousef, L.D. Rippon, C. Prévost, S.L. Shah, R.B. Gopaluni. The arc loss challenge: A novel industrial benchmark for process analytics and machine learning. Journal of Process Control. 2023. https://doi.org/10.1016/j.jprocont.2023.103023.

65. A. Harrouz, H. Salem, H. Toubakh, R.M. Kafi, M.S. Mouchaweh. Fault prognosis of subsurface safety valve system with limited real data using self-adaptive neural network. Evolving Systems. 2023. https://doi.org/10.1007/s12530-023-09525-w.

66. E. Jovicic, D. Primorac, M. Cupic, A. Jovic. Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review. IEEE Access. 2023. https://doi.org/10.1109/ACCESS.2023.3295113.

67. R.M.F.U. Foronda, V.M. Fracassio, R.B. Santos, B.F. Santos. Statistical Analysis in Database of Offshore Naturally Flowing Wells with Abnormal Events. Chemical Engineering Transactions. 2023. https://doi.org/10.3303/CET2399101.

68. M.A. Sahraoui, C. Rahmoune, M. Zair, F. Gougam, A. Damou. Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy. Journal of Process Mechanical Engineering. 2023. https://doi.org/10.1177/09544089231213778.

69. W.F. Junior, K.S. Komati, K.A.S. Gazolli. Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset. Journal of Petroleum Exploration and Production Technology. 2023. https://doi.org/10.1007/s13202-023-01710-6.

70. P.E. Aranha, N.A. Policarpo, M.A. Sampaio. Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production. Journal of Petroleum Exploration and Production Technology. 2023. https://doi.org/10.1007/s13202-023-01720-4.

71. R.E.V. Vargas, R.L.A. Pinto. The 3W Project and its Strategy to Foster the Development of Data-Driven Solutions for the Offshore Sector. Offshore Technology Conference Brasil. 2023. https://doi.org/10.4043/32875-MS.

72. C. Shyalika, R. Wickramarachchi, A. Sheth. A Comprehensive Survey on Rare Event Prediction. arXiv. 2023. https://doi.org/10.48550/arXiv.2309.11356.

73. M.A. Farahani, M.R. McCormick, R. Harik, T. Wuest. Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms. arXiv. 2023. https://doi.org/10.48550/arXiv.2310.02812.

74. P.E. Aranha, L.G.O. Lopes, E.S.P. Sobrinho; I.M.N. Oliveira, J.P.N. Araújo, B.B. Santos; E.T.L. Junior, T.B. Silva, T.M.A. Vieira, W.W.M. Lira, N.A. Policarpo, M.A. Sampaio. A System to Detect Oilwell Anomalies Using Deep Learning and Decision Diagram Dual Approach. SPE Journal. 2023. https://doi.org/10.2118/218017-PA.

75. L. Liu, J. Li, Z. Niu, W. Zhang, J.C. Xue, H. Xu. Efficient Time-Series Data Delivery in IoT with Xender. IEEE Transactions on Mobile Computing. 2023. https://doi.org/10.1109/TMC.2023.3296608.

76. X. Deng; H. Yin. Industrial Process Fault Diagnosis in Case of Missing Sensor Data. Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). 2023. https://doi.org/10.1109/SAFEPROCESS58597.2023.10295829.

77. Y. Li. Predictive Analysis and Critical Event Monitoring in Large Dynamic Networks. University of Massachusetts Lowell. Doctoral thesis. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_yan_li.pdf.

78. A. Das, A. Aiken. Prolego: Time-Series Analysis for Predicting Failures in Complex Systems. IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS. 2023. https://doi.org/10.1109/ACSOS58161.2023.00025.

79. Y. Qu, B. Zhou, A. Waaler, D. Cameron. Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. 2024. Lecture Notes in Computer Science. http://dx.doi.org/10.1007/978-981-99-7025-4_41.

80. A.P.F. Machado, C.J. Munaro, P.M. Ciarelli, R.E.V. Vargas. Time series clustering to improve one-class classifier performance. Expert Systems with Applications. 2024. https://doi.org/10.1016/j.eswa.2023.122895.

81. O. Khankishiyev, S. Salehi, H. Karami, V. Mammadzada. Identification of Undesirable Events in Geothermal Fluid/Steam Production using Machine Learning. 49th Workshop on Geothermal Reservoir Engineering. 2024. https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2024/Khankishiyev1.pdf.

82. F.M. Varejão, L.H.S. Mello, M.P. Ribeiro, T.O. Santos, A.L. Rodrigues. An open source experimental framework and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration. Knowledge-Based Systems. 2024. https://doi.org/10.1016/j.knosys.2024.111452.

83. A. Melo, M.M. Câmara, J.C. Pinto. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey. Processes. 2024. https://doi.org/10.3390/pr12020251.

84. A.P.F. Machado. Methodologies to Improve One-Class Classifier Performance Applied to Multivariate Time Series. Universidade Federal do Espírito Santo. Doctoral thesis. 2024. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_andre_machado.pdf.

85. E.M. Turan. Advances in Optimisation and Machine Learning for Process Systems Engineering. Norwegian University of Science and Technology. Doctoral thesis. 2024. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_evren_turan.pdf.

86. T.L.B. Dias, M.A. Marins, C.L. Pagliari, R.M.E. Barbosa, M.L.R. de Campos, E.A.B. Silva, S.L.Netto. Development of Oilwell Fault Classifiers Using a Wavelet-Based Multivariable Approach in a Modular Architecture. SPE Journal. 2024. https://doi.org/10.2118/221463-PA.

87. A. Markaj, M. Mercangöz, A. Fay. Design and implementation of an Autonomous Systems Training Environment framework for control algorithm evaluation in autonomous plant operation. Computers & Chemical Engineering. 2024. https://doi.org/10.1016/j.compchemeng.2024.108798.

88. A. Melo, T.S.M. Lemos, R.M. Soares, D. Spina, N. Clavijo, L.F.O. Campos, M.M. Câmara, T. Feital, T.K. Anzai, P.H. Thompson, F.C. Diehl, J.C.Pinto. BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis. Digital Chemical Engineering. 2024. https://doi.org/10.1016/j.dche.2024.100182.

89. R. Villamil, J. May, R. Fallgatter, R. Vargas, A. Nakashima, G. Peixer, J. Lozano, J. Barbosa. Assessment of deep-learning techniques for anomaly detection in offshore oil wells. Brazilian Congress of Thermal Sciences and Engineering. 2024. http://dx.doi.org/10.26678/ABCM.ENCIT2024.CIT24-0603.

90. A.O. Ifenaike, O.B. Oluwadare. Advancing Drilling Safety: Automated Anomaly Detection in Well Control Using Machine Learning Techniques. SPE Nigeria Annual International Conference and Exhibition. 2024. https://doi.org/10.2118/221626-MS.

91. G. Bayazitova, M. Anastasiadou, V.D. Santos. Oil and gas flow anomaly detection on offshore naturally flowing wells using deep neural networks. Geoenergy Science and Engineering. 2024. https://doi.org/10.1016/j.geoen.2024.213240.

92. A.A.M. Azevêdo. Análise exploratória do conjunto de dados 3W para detecção de falhas de operação de poços de petróleo, usando técnicas de aprendizado de máquina. Universidade Federal do Rio de Janeiro. Master's degree dissertation. 2024. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_antonio_azevedo.pdf.

93. H. Zhou, H. Yin, Y. Qin, C. Yuen. Industrial Fault Diagnosis With Incremental Learning Capability Under Varying Sensory Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2024. https://doi.org/10.1109/TSMC.2024.3500019.

94. M.A. Farahani, M.R. McCormick, R. Harik, T. Wuest. Time-series classification in smart manufacturing systems: An experimental evaluation of state-of-the-art machine learning algorithms. Robotics and Computer-Integrated Manufacturing. 2025. https://doi.org/10.1016/j.rcim.2024.102839.

95. D. Semwogerere, S. Sangesland, J. Vatn, A. Pavlov, D. Colombo. Well integrity and late life extension - A current industry state of practice and literature review. Geoenergy Science and Engineering. 2025. https://doi.org/10.1016/j.geoen.2024.213419.