How to cite this paper
Ward, T., Jenab, K & Ortega-Moody, J. (2024). Machine learning models for condition-based maintenance with regular truncated signals.Decision Science Letters , 13(1), 197-210.
Refrences
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Akouemo, H. N., & Povinelli, R. J. (2017). Data improving in time series using ARX and Ann Models. IEEE Transactions on Power Systems, 32(5), 3352–3359. https://doi.org/10.1109/tpwrs.2017.2656939
Alabadla, M., Sidi, F., Ishak, I., Ibrahim, H., Affendey, L. S., Che Ani, Z., Jabar, M. A., Bukar, U. A., Devaraj, N. K., Muda, A. S., Tharek, A., Omar, N., & Jaya, M. I. (2022). Systematic review of using machine learning in imputing missing values. IEEE Access, 10, 44483–44502. https://doi.org/10.1109/access.2022.3160841
Alwan, W., Ngadiman, N. H. A., & Hassan, A. (2022). Ensemble classifier with missing data in control chart patterns. Proceedings of the International Conference on Industrial Engineering and Operations Management. https://doi.org/10.46254/au01.20220420
Appoh, F., & Yunusa-Kaltungo, A. (2021). Risk-informed support vector machine regression model for component replacement—a case study of Railway Flange Lubricator. IEEE Access, 9, 85418–85430. https://doi.org/10.1109/access.2021.3088586
Barnes, S. A., Larsen, M. D., Schroeder, D., Hanson, A., & Decker, P. A. (2010). Missing data assumptions and methods in a smoking cessation study. Addiction, 105(3), 431–437. https://doi.org/10.1111/j.1360-0443.2009.02809.x
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards Sustainable Smart Manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Du, J., Hu, M., & Zhang, W. (2020). Missing data problem in the monitoring system: A Review. IEEE Sensors Journal, 20(23), 13984–13998. https://doi.org/10.1109/jsen.2020.3009265
Emir, Ş. (2023). An investigation of anomaly detection methods in machine learning for high dimensional datasets. Global Studies on Management Information Systems, 227–254. https://doi.org/10.26650/b/ss28et06.2023.006.10
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hartini, E. (2017). Implementation of missing values handling method for evaluating the System/Component Maintenance Historical Data. Journal of Nuclear Reactor Technology, 19(1), 11. https://doi.org/10.17146/tdm.2017.19.1.3159
Hey, J., Malloy, A. C., Martinez-Botas, R., & Lamperth, M. (2016). Online monitoring of electromagnetic losses in an electric motor indirectly through temperature measurement. IEEE Transactions on Energy Conversion, 31(4), 1347–1355. https://doi.org/10.1109/tec.2016.2562029
Jha, S., Cui, S., Xu, T., Enos, J., Showerman, M., Dalton, M., Kalbarczyk, Z. T., Kramer, W. T., & Iyer, R. K. (2019). Live Forensics for Distributed Storage Systems. arXiv. https://arxiv.org/abs/1907.10203
Jakobsen, J. C., Gluud, C., Wetterslev, J., & Winkel, P. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials – A practical guide with flowcharts. BMC Medical Research Methodology, 17(1). https://doi.org/10.1186/s12874-017-0442-1
Lavin, A., & Ahmad, S. (2015). Evaluating real-time anomaly detection algorithms - the Numenta Anomaly Benchmark. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/icmla.2015.141
Li, Z., & He, Q. (2015). Prediction of railcar remaining useful life by multiple data source fusion. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2226–2235. https://doi.org/10.1109/tits.2015.2400424
Loukopoulos, P., Sampath, S., Pilidis, P., Zolkiewski, G., Bennett, I., Duan, F., & Mba, D. (2016). Dealing with missing data for prognostic purposes. 2016 Prognostics and System Health Management Conference (PHM-Chengdu). https://doi.org/10.1109/phm.2016.7819934
Martins, A. B., Fonseca, I., Farinha, J. T., Reis, J., & Marques Cardoso, A. J. (2022). Prediction maintenance based on vibration analysis and deep learning – A case study of a drying press supported on a hidden Markov model. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4194601
McKinney, W. (2010). Data Structures for Statistical Computing in python. Proceedings of the Python in Science Conference. https://doi.org/10.25080/majora-92bf1922-00a
Merkt, O. (2019). On the use of predictive models for improving the quality of Industrial Maintenance: An analytical literature review of Maintenance Strategies. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems. https://doi.org/10.15439/2019f101
Osman, M. S., Abu-Mahfouz, A. M., & Page, P. R. (2018). A survey on data imputation techniques: Water distribution system as a use case. IEEE Access, 6, 63279–63291. https://doi.org/10.1109/access.2018.2877269
Pedersen, A., Mikkelsen, E., Cronin-Fenton, D., Kristensen, N., Pham, T. M., Pedersen, L., & Petersen, I. (2017). Missing data and multiple imputation in clinical epidemiological research. Clinical Epidemiology, 9, 157–166. https://doi.org/10.2147/clep.s129785
Rafsunjani, S., Safa, R. S., Imran, A. A., Rahim, S., & Nandi, D. (2019). An empirical comparison of missing value imputation techniques on APS Failure Prediction. International Journal of Information Technology and Computer Science, 11(2), 21–29. https://doi.org/10.5815/ijitcs.2019.02.03
Schmidt, F. (2020). Anomaly Detection in Cloud Computing Environments. https://doi.org/10.14279/depositonce-10393
Song, C., Zheng, Z., & Liu, K. (2022). Building local models for flexible degradation modeling and Prognostics. IEEE Transactions on Automation Science and Engineering, 19(4), 3483–3495. https://doi.org/10.1109/tase.2021.3124144
Song, I., Yang, Y., Im, J., Tong, T., Ceylan, H., & Cho, I. H. (2020). Impacts of fractional hot-deck imputation on learning and prediction of Engineering Data. IEEE Transactions on Knowledge and Data Engineering, 32(12), 2363–2373. https://doi.org/10.1109/tkde.2019.2922638
Srimedha, B. C., Naveen Raj, R., & Mayya, V. (2022). A comprehensive machine learning based pipeline for an accurate early prediction of sepsis in ICU. IEEE Access, 10, 105120–105132. https://doi.org/10.1109/access.2022.3210575
The Pandas Development Team. (2020). pandas-dev/pandas: Pandas (Version 2.1.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3509134
Wang, M., Yang, C., Zhao, F., Min, F., & Wang, X. (2023). Cost-sensitive active learning for incomplete data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(1), 405–416. https://doi.org/10.1109/tsmc.2022.3182122
Zhang, Z.-W., Tian, H.-P., Yan, L.-Z., Martin, A., & Zhou, K. (2022). Learning a credal classifier with optimized and adaptive multiestimation for missing data imputation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(7), 4092–4104. https://doi.org/10.1109/tsmc.2021.3090210
Adriana Mercioni, M., & Holban, S. (2022). Prediction of machine temperature system failure using a novel activation function. 2022 International Symposium on Electronics and Telecommunications (ISETC). https://doi.org/10.1109/isetc56213.2022.10010046
Akouemo, H. N., & Povinelli, R. J. (2017). Data improving in time series using ARX and Ann Models. IEEE Transactions on Power Systems, 32(5), 3352–3359. https://doi.org/10.1109/tpwrs.2017.2656939
Alabadla, M., Sidi, F., Ishak, I., Ibrahim, H., Affendey, L. S., Che Ani, Z., Jabar, M. A., Bukar, U. A., Devaraj, N. K., Muda, A. S., Tharek, A., Omar, N., & Jaya, M. I. (2022). Systematic review of using machine learning in imputing missing values. IEEE Access, 10, 44483–44502. https://doi.org/10.1109/access.2022.3160841
Alwan, W., Ngadiman, N. H. A., & Hassan, A. (2022). Ensemble classifier with missing data in control chart patterns. Proceedings of the International Conference on Industrial Engineering and Operations Management. https://doi.org/10.46254/au01.20220420
Appoh, F., & Yunusa-Kaltungo, A. (2021). Risk-informed support vector machine regression model for component replacement—a case study of Railway Flange Lubricator. IEEE Access, 9, 85418–85430. https://doi.org/10.1109/access.2021.3088586
Barnes, S. A., Larsen, M. D., Schroeder, D., Hanson, A., & Decker, P. A. (2010). Missing data assumptions and methods in a smoking cessation study. Addiction, 105(3), 431–437. https://doi.org/10.1111/j.1360-0443.2009.02809.x
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards Sustainable Smart Manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Du, J., Hu, M., & Zhang, W. (2020). Missing data problem in the monitoring system: A Review. IEEE Sensors Journal, 20(23), 13984–13998. https://doi.org/10.1109/jsen.2020.3009265
Emir, Ş. (2023). An investigation of anomaly detection methods in machine learning for high dimensional datasets. Global Studies on Management Information Systems, 227–254. https://doi.org/10.26650/b/ss28et06.2023.006.10
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hartini, E. (2017). Implementation of missing values handling method for evaluating the System/Component Maintenance Historical Data. Journal of Nuclear Reactor Technology, 19(1), 11. https://doi.org/10.17146/tdm.2017.19.1.3159
Hey, J., Malloy, A. C., Martinez-Botas, R., & Lamperth, M. (2016). Online monitoring of electromagnetic losses in an electric motor indirectly through temperature measurement. IEEE Transactions on Energy Conversion, 31(4), 1347–1355. https://doi.org/10.1109/tec.2016.2562029
Jha, S., Cui, S., Xu, T., Enos, J., Showerman, M., Dalton, M., Kalbarczyk, Z. T., Kramer, W. T., & Iyer, R. K. (2019). Live Forensics for Distributed Storage Systems. arXiv. https://arxiv.org/abs/1907.10203
Jakobsen, J. C., Gluud, C., Wetterslev, J., & Winkel, P. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials – A practical guide with flowcharts. BMC Medical Research Methodology, 17(1). https://doi.org/10.1186/s12874-017-0442-1
Lavin, A., & Ahmad, S. (2015). Evaluating real-time anomaly detection algorithms - the Numenta Anomaly Benchmark. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/icmla.2015.141
Li, Z., & He, Q. (2015). Prediction of railcar remaining useful life by multiple data source fusion. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2226–2235. https://doi.org/10.1109/tits.2015.2400424
Loukopoulos, P., Sampath, S., Pilidis, P., Zolkiewski, G., Bennett, I., Duan, F., & Mba, D. (2016). Dealing with missing data for prognostic purposes. 2016 Prognostics and System Health Management Conference (PHM-Chengdu). https://doi.org/10.1109/phm.2016.7819934
Martins, A. B., Fonseca, I., Farinha, J. T., Reis, J., & Marques Cardoso, A. J. (2022). Prediction maintenance based on vibration analysis and deep learning – A case study of a drying press supported on a hidden Markov model. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4194601
McKinney, W. (2010). Data Structures for Statistical Computing in python. Proceedings of the Python in Science Conference. https://doi.org/10.25080/majora-92bf1922-00a
Merkt, O. (2019). On the use of predictive models for improving the quality of Industrial Maintenance: An analytical literature review of Maintenance Strategies. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems. https://doi.org/10.15439/2019f101
Osman, M. S., Abu-Mahfouz, A. M., & Page, P. R. (2018). A survey on data imputation techniques: Water distribution system as a use case. IEEE Access, 6, 63279–63291. https://doi.org/10.1109/access.2018.2877269
Pedersen, A., Mikkelsen, E., Cronin-Fenton, D., Kristensen, N., Pham, T. M., Pedersen, L., & Petersen, I. (2017). Missing data and multiple imputation in clinical epidemiological research. Clinical Epidemiology, 9, 157–166. https://doi.org/10.2147/clep.s129785
Rafsunjani, S., Safa, R. S., Imran, A. A., Rahim, S., & Nandi, D. (2019). An empirical comparison of missing value imputation techniques on APS Failure Prediction. International Journal of Information Technology and Computer Science, 11(2), 21–29. https://doi.org/10.5815/ijitcs.2019.02.03
Schmidt, F. (2020). Anomaly Detection in Cloud Computing Environments. https://doi.org/10.14279/depositonce-10393
Song, C., Zheng, Z., & Liu, K. (2022). Building local models for flexible degradation modeling and Prognostics. IEEE Transactions on Automation Science and Engineering, 19(4), 3483–3495. https://doi.org/10.1109/tase.2021.3124144
Song, I., Yang, Y., Im, J., Tong, T., Ceylan, H., & Cho, I. H. (2020). Impacts of fractional hot-deck imputation on learning and prediction of Engineering Data. IEEE Transactions on Knowledge and Data Engineering, 32(12), 2363–2373. https://doi.org/10.1109/tkde.2019.2922638
Srimedha, B. C., Naveen Raj, R., & Mayya, V. (2022). A comprehensive machine learning based pipeline for an accurate early prediction of sepsis in ICU. IEEE Access, 10, 105120–105132. https://doi.org/10.1109/access.2022.3210575
The Pandas Development Team. (2020). pandas-dev/pandas: Pandas (Version 2.1.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3509134
Wang, M., Yang, C., Zhao, F., Min, F., & Wang, X. (2023). Cost-sensitive active learning for incomplete data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(1), 405–416. https://doi.org/10.1109/tsmc.2022.3182122
Zhang, Z.-W., Tian, H.-P., Yan, L.-Z., Martin, A., & Zhou, K. (2022). Learning a credal classifier with optimized and adaptive multiestimation for missing data imputation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(7), 4092–4104. https://doi.org/10.1109/tsmc.2021.3090210