Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 5, 2024
Douglas Baptista de Souza Bruno Paes Leao

Abstract

This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions.

How to Cite

Baptista de Souza, D., & Leao, B. P. (2024). Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4145
Abstract 50 | PDF Downloads 62

##plugins.themes.bootstrap3.article.details##

Keywords

data augmentation, autoregressive models, failure prognostics, data-centric PHM

References
Arias Chao, M., Kulkarni, C., Goebel, K., & Fink, O. (n.d.). Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data, 6(5).

AsSadhan, B., Zeb, K., Al-Muhtadi, J., & Alshebeili, S. (2017). Anomaly detection based on lrd behavior analysis of decomposed control and data planes network traffic using soss and farima models. IEEE Access, 5, 13501-13519.

Biggio, L., & Kastanis, I. (2020). Prognostics and health management of industrial assets: Current progress and road ahead. Front. Artif. Intell., 3, 1-24.

Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a Python package). Neurocomputing,
307, 72-77.

Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. In International Conference on Machine Learning (PMLR 2024) (Vol. 235, p. 10148-10167). Vienna, Austria.

Deistler, M., & Scherrer, W. (2022). Time series models (1st ed.).

Vienna, Austria: Springer. de Oliveira, F. A. C., Niemi, A., GarcÅLıa-Ortiz, A., & Torres, F. S. (2023). Partial camera obstruction detection using single value image metrics and data augmentation. In International Conference on System Reliability and Safety (ICSRS 2023) (p. 292-299). Venice, Italy.

de Souza, D. B., Chanussot, J., & Favre, A.-C. (2014). On selecting relevant intrinsic mode functions in empirical mode decomposition: An energy-based approach. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) (p. 325-329). Florence, Italy.

de Souza, D. B., Chanussot, J., Favre, A.-C., & Borgnat, P. (2012). A modified time-frequency method for testing wide-sense stationarity. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012) (p. 3409-3412). Kyoto, Japan.

de Souza, D. B., Chanussot, J., Favre, A.-C., & Borgnat, P. (2014). A new nonparametric method for testing stationarity based on trend analysis in the time marginal distribution. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) (p. 320-324). Florence, Italy.

de Souza, D. B., Chanussot, J., Favre, A.-C., & Borgnat, P. (2018). A nonparametric test for slowly-varying nonstationarities. Signal Process., 143, 241-252.

de Souza, D. B., Chanussot, J., Favre, A.-C., & Borgnat, P. (2019). An improved stationarity test based on surrogates. IEEE Signal Process. Lett., 26(10), 1431-1435.

de Souza, D. B., Kuhn, E. V., & Seara, R. (2019). A time varying autoregressive model for characterizing nonstationary processes. IEEE Signal Process. Lett., 26(1), 134-138.

de Souza, D. B., & Leao, B. P. (2023). Data augmentation of sensor time series using time-varying autoregressive processes. In Annual Conference of the Prognostics and Health Management Society (PHM 2023) (Vol. 15, p. 1-10). Salt Lake City, UT.

Garan,M., Tidriri, K., & Kovalenko, I. (2022). A data-centric machine learning methodology: Application on predictive maintenance of wind turbines. Energies, 15(3), 1- 21.

Jiang, X., & Ge, Z. (2021). Data Augmentation Classifier for Imbalanced Fault Classification. IEEE Trans. Autom. Sci., 18(3), 1206-1217.

Kay, S. (2008). A new nonstationarity detector. IEEE Trans. Signal Process., 56(4), 1440-1451.

Kefalas, M., Baratchi, M., Apostolidis, A., van den Herik, D., & BÅNack, T. (2021). Automated machine learning for remaining useful life estimation of aircraft engines. In 2021 ieee international conference on prognostics and health management (icphm) (pp. 1–9).

Kim, S., Choi, J.-H., & Kim, N. H. (2021). Challenges and opportunities of system-level prognostics. Sensors, 21(22), 1-25.

Kim, S., Kim, N. H., & Choi, J.-H. (2020). Prediction of remaining useful life by data augmentation technique based on dynamic time warping. Mech. Syst. Signal Process., 136, 106486.

Kwak, M., & Lee, J. (2023). Diagnosis-based domain adaptive design using designable data augmentation and bayesian transfer learning: Target design estimation and validation. Appl. Soft Comput., 143, 110459.

Le, T. T., Fu, W., & Moore, J. H. (2020). Scaling tree based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, 36(1), 250–256.

Leao, B. P., Fradkin, D., Lan, T., &Wang, J. (2021). Unleashing the power of industrial big data through scalable manual labeling. In NeurIPS Data-Centric AI Workshop (p. 1-5).

Li, H., Zhang, Z., & Zhang, C. (2023). Data augmentation via variational mode reconstruction and its application in few-shot fault diagnosis of rolling bearings. Measurement, 217, 113062.

Li, X. Y., Cheng, D. J., Fang, X. F., Zhang, C. Y., & Wang, Y. F. (2024). A novel data augmentation strategy for aeroengine multitask prognosis based on degradation behavior extrapolation and diversity-usability trade-off. Reliability Engineering & System Safety, 249, 110238. doi: https://doi.org/10.1016/j.ress.2024 .110238

Matei, I., Zhenirovskyy, M., de Kleer, J., & Feldman, A. (2018). Classification-based diagnosis using synthetic data from uncertain models. In Annual Conference of the Prognostics and Health Management Society (PHM 2018) (p. 1-8). Philadelphia, PA.

Pachori, R. B., & Sircar, P. (2008). EEG signal analysis using FB expansion and second-order linear TVAR process. Signal Process., 88(2), 415-420.

Ramasso, E., & Saxena, A. (2014). Review and analysis of algorithmic approaches developed for prognostics on cmapss dataset. In Annual conference of the prognostics and health management society 2014.

Rao, T. S. (1970). The fitting of non-stationary time series models with time-dependent parameters. J. R. Statist. Soc., 32, 312-322.

Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA Ames Prognostics Data repository, NASA Ames Research Center, Moffett Field.

Shen, B., Yao, L., Jiang, X., Yang, Z., & Zeng, J. (2023). Time series data augmentation classifier for industrial process imbalanced fault diagnosis. In IEEE Data Driven Control and Learning Systems Conference (DDCLS 2023) (p. 1392-1397). Xiangtan, China.

Sodsri, C. (2003). Time-varying autoregressive modelling for nonstationary acoustic signal and its frequency analysis (Unpublished doctoral dissertation). Pennsylvania State University.

Taghiyarrenani, Z., & Berenji, A. (2022). Noise-robust representation for fault identification with limited data via data augmentation. In European Conference of the Prognostics and Health Management Society (PHME 2022) (p. 473-479). Turin, Italy.

Wang, D., Dong, Y., Wang, H., & Tang, G. (2023). Limited Fault Data Augmentation With Compressed Sensing for Bearing Fault Diagnosis. IEEE Sens. J., 23(13), 14499-14511.

Yang, A., Lu, C., Yu, W., Hu, J., Nakanishi, Y., & Wu, M. (2023). Data Augmentation Considering Distribution Discrepancy for Fault Diagnosis of Drilling Process With Limited Samples. IEEE Trans. Ind. Electron., 70(11), 11774-11783.

Yang, Y., Zheng, H., & Zhang, R. (2017). Prediction and analysis of aircraft failure rate based on sarima model. In IEEE International Conference on Computational Intelligence and Applications (ICCIA 2017) (p. 567- 571). Beijing, China.

Zhang, X., Chowdhury, R. R., Gupta, R. K., & Shang, J. (2024). Large language models for time series: A survey. In International Joint Conference on Artificial Intelligence (IJCAI 2024) (p. 1-9). Jeju, South Korea.
Section
Industry Experience Papers