Deep Regression Network with Prediction Confidence in Time Series Application for Asset Health Estimation



Published Oct 26, 2023
Hao Huang Arun Subramanian Abhinav Saxena Nurali Virani Naresh Iyer


Many works have been focused in developing detection, monitoring and prediction routines for asset health estimation system. Classic machine learning based models benefit from quality of physics-informed features available from domain knowledge. This, however, can be labor intensive and is limited by quality of features developed through available knowledge. Deep learning based approach, if successful, can alleviate this laborious step. On the other hand, users often need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. In this work, we propose a deep learning based regression network that output both prediction value and confidence score for asset health estimation in short intermittent transients time series application. In the experimental study, we show that our model has low prediction error given short intermittent transients multivariate time series as input. Furthermore, our model also provides a confidence score for each prediction that is highly negatively correlated with true prediction error. Experiments show that by setting an acceptance threshold on confidence score, our model can reach an averaged improvement of 20% on the prediction quality with 90% coverage.

How to Cite

Huang, H., Subramanian, A., Saxena, A., Virani, N., & Iyer, N. (2023). Deep Regression Network with Prediction Confidence in Time Series Application for Asset Health Estimation. Annual Conference of the PHM Society, 15(1).
Abstract 265 | PDF Downloads 153



Time series regression, confidence score, Asset Health Estimation

AAbdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76, 243–297.

Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

Bhushan, C., Yang, Z., Virani, N., & Iyer, N. (2020). Variational encoder-based reliable classification. In 2020 ieee international conference on image processing (icip) (pp. 1941–1945).

Briesemeister, S., Rahnenf ̈uhrer, J., & Kohlbacher, O. (2012). No longer confidential: estimating the confidence of individual regression predictions. PloS one, 7(11), e48723.

Brokamp, C., Rao, M., Ryan, P., & Jandarov, R. (2017). A comparison of resampling and recursive partitioning methods in random forest for estimating the asymptotic variance using the infinitesimal jackknife. stat, 6(1), 360–372.

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023). Swin-unet: Unet-like pure transformer for medical image segmentation. In Computer vision–eccv 2022 workshops: Tel aviv, israel, October 23–27, 2022, proceedings, part iii (pp. 205–218).

de Bie, K., Lucic, A., & Haned, H. (2021). To trust or not to trust a regressor: Estimating and explaining trustworthiness of regression predictions. arXiv preprint arXiv:2104.06982.

Dong, M., et al. (2010). A tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction: concepts, models, and algorithms. Mathematical Problems in Engineering, 2010.

Ellefsen, A. L., Æsøy, V., Ushakov, S., & Zhang, H. (2019). A comprehensive survey of prognostics and health management based on deep learning for autonomous ships. IEEE Transactions on Reliability, 68(2), 720–740.

Finch, S., & Cumming, G. (2009). Putting research in context: Understanding confidence intervals from one or more studies. Journal of Pediatric Psychology, 34(9), 903–916.

Fink, O., Wang, Q., Svensen, 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.

Franceschi, J.-Y., Dieuleveut, A., & Jaggi, M. (2019). Unsupervised scalable representation learning for multivariate time series. arXiv preprint arXiv:1901.10738.

GE-Research, & University of Tennessee, U., Knoxville. (2023). Servomotor-driven ballscrew mechanism degradation data set. Retrieved from dataset/

Ghobrial, A., Asgari, H., & Eder, K. (2023). Towards a measure of trustworthiness to evaluate cnns during operation. arXiv preprint arXiv:2301.08839.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778).

Iyer, N., Virani, N., Yang, Z., & Saxena, A. (2022). Mixed initiative approach for reliable tagging of maintenance records with machine learning. In Annual conference of the phm society (Vol. 14).

Jiang, H., Kim, B., Guan, M., & Gupta, M. (2018). To trust or not to trust a classifier. Advances in neural information processing systems, 31.

Liao, L., & Ahn, H.-i. (2016). Combining deep learning and survival analysis for asset health management. International Journal of Prognostics and Health Management, 7(4).

Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., . . . Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.

Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement, 163, 107929.

Virani, N., Iyer, N., & Yang, Z. (2020). Justification-based reliability in machine learning. In Proceedings of the aaai conference on artificial intelligence (Vol. 34, pp. 6078–6085).

Wager, S., Hastie, T., & Efron, B. (2014). Confidence intervals for random forests: The jackknife and the infinitesimal jackknife. The Journal of Machine Learning Research, 15(1), 1625–1651.

Wang, H., Cao, P., Wang, J., & Zaiane, O. R. (2022). Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In Proceedings of the aaai conference on artificial intelligence (Vol. 36, pp. 2441–2449).

Yang, Y., Gong, Z., & U, L. H. (2011). Identifying points of interest by self-tuning clustering. In Proceedings of the 34th international acm sigir conference (pp. 883–892).

Yucesan, Y. A., Dourado, A., & Viana, F. A. (2021). A survey of modeling for prognosis and health management of industrial equipment. Advanced Engineering Informatics, 50, 101404.

Zelnik-Manor, L., & Perona, P. (2004). Self-tuning spectral clustering. Advances in neural information processing systems, 17.

Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A review on deep learning applications in prognostics and health management. Ieee Access, 7, 162415–162438.
Industry Experience Papers

Most read articles by the same author(s)

1 2 3 > >>