Difference between revisions of "Transfer Learning for Machine Diagnostics and Prognostics"
YuantaoFan (Talk | contribs) (Created page with "{{StudentProjectTemplate |Summary=Propose and optimise Deep (Adversarial) Neural Networks based models for machine diagnostics and prognostics under inductive and transductive...") |
YuantaoFan (Talk | contribs) |
||
| Line 25: | Line 25: | ||
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging. | Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging. | ||
| − | Therefore, machine diagnostic and prognostic methods needed | + | Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles. |
Revision as of 01:38, 2 October 2019
| Title | Transfer Learning for Machine Diagnostics and Prognostics |
|---|---|
| Summary | Propose and optimise Deep (Adversarial) Neural Networks based models for machine diagnostics and prognostics under inductive and transductive transfer learning settings |
| Keywords | Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics |
| TimeFrame | Fall 2019 |
| References | [[References::[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.
[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9. [3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014). [4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305. [5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019). [6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019). [7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019). [8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.]] |
| Prerequisites | Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms |
| Author | |
| Supervisor | Yuantao Fan |
| Level | Master |
| Status | Draft |
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.
Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.
