Remaining useful life prediction: from statistical modeling to federated approach
Smart manufacturing is getting increasingly popular in different areas, including aircraft systems, uninterruptible power supply systems, hydraulic systems, etc. Meanwhile, the capability of intelligent machines doing edge computing is growing rapidly. Predictive maintenance is applied to differe...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156410 |
| _version_ | 1826124432228220928 |
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| author | Gan, Xin |
| author2 | Wen Yonggang |
| author_facet | Wen Yonggang Gan, Xin |
| author_sort | Gan, Xin |
| collection | NTU |
| description | Smart manufacturing is getting increasingly popular in different areas, including aircraft
systems, uninterruptible power supply systems, hydraulic systems, etc. Meanwhile,
the capability of intelligent machines doing edge computing is growing rapidly.
Predictive maintenance is applied to different areas to use edge data to reduce risks
of operating machines since any unplanned downtime of the machine could result in
unmeasurable loss. Remaining Useful Life Prediction, determines whether or when
machine fails based on current and past data representing system conditions, is an essential
component of predictive maintenance. Statistical models and machine learning
techniques are able to do Remaining Useful Life (RUL) Prediction. We studied both
methods and present a demo to show how can we implement a Remaining Useful Life
Prediction system and do risk analysis. However, during the development of the Remaining
Useful Life Prediction system, we found that with the increasingly stringent
privacy restrictions, the centralization of data from edge devices are limited in reality.
The situation is further aggravated by components isolation. Different machine
components are not able to share data with each other since they belong to different
manufacturers or have component-specific sensitivity. To tackle these privacy matters,
we present Federated Remaining Useful Life Prediction(FedRUL) for isolated system
components, which implements federated learning, a distributed training method, to
Remaining Useful Life Prediction. FedRUL uses machine learning method to do RUL
prediction. FedRUL preserves data privacy by aggregating partial model parameters
instead of data, from clients to a central server. To make it feasible and with good performance:
(1) We add local dimension transfer layer to transfer all input to the same
dimension; (2) We only aggregate the layers except local dimension transfer layer. (3)
We apply momentum update on model parameter aggregation to address the importance
of the local models to optimize the performance. In this work, we use intuitive
methods to implement a Remaining Useful Life Prediction system, construct a basic
analysis for Remaining Useful Life Prediction addressing component isolation, apply
FedRUL to alleviate the performance drop introduced by component isolation. |
| first_indexed | 2024-10-01T06:20:32Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156410 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:20:32Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1564102022-05-04T08:10:51Z Remaining useful life prediction: from statistical modeling to federated approach Gan, Xin Wen Yonggang School of Computer Science and Engineering YGWEN@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Smart manufacturing is getting increasingly popular in different areas, including aircraft systems, uninterruptible power supply systems, hydraulic systems, etc. Meanwhile, the capability of intelligent machines doing edge computing is growing rapidly. Predictive maintenance is applied to different areas to use edge data to reduce risks of operating machines since any unplanned downtime of the machine could result in unmeasurable loss. Remaining Useful Life Prediction, determines whether or when machine fails based on current and past data representing system conditions, is an essential component of predictive maintenance. Statistical models and machine learning techniques are able to do Remaining Useful Life (RUL) Prediction. We studied both methods and present a demo to show how can we implement a Remaining Useful Life Prediction system and do risk analysis. However, during the development of the Remaining Useful Life Prediction system, we found that with the increasingly stringent privacy restrictions, the centralization of data from edge devices are limited in reality. The situation is further aggravated by components isolation. Different machine components are not able to share data with each other since they belong to different manufacturers or have component-specific sensitivity. To tackle these privacy matters, we present Federated Remaining Useful Life Prediction(FedRUL) for isolated system components, which implements federated learning, a distributed training method, to Remaining Useful Life Prediction. FedRUL uses machine learning method to do RUL prediction. FedRUL preserves data privacy by aggregating partial model parameters instead of data, from clients to a central server. To make it feasible and with good performance: (1) We add local dimension transfer layer to transfer all input to the same dimension; (2) We only aggregate the layers except local dimension transfer layer. (3) We apply momentum update on model parameter aggregation to address the importance of the local models to optimize the performance. In this work, we use intuitive methods to implement a Remaining Useful Life Prediction system, construct a basic analysis for Remaining Useful Life Prediction addressing component isolation, apply FedRUL to alleviate the performance drop introduced by component isolation. Bachelor of Engineering (Computer Science) 2022-04-16T10:57:21Z 2022-04-16T10:57:21Z 2022 Final Year Project (FYP) Gan, X. (2022). Remaining useful life prediction: from statistical modeling to federated approach. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156410 https://hdl.handle.net/10356/156410 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gan, Xin Remaining useful life prediction: from statistical modeling to federated approach |
| title | Remaining useful life prediction: from statistical modeling to federated approach |
| title_full | Remaining useful life prediction: from statistical modeling to federated approach |
| title_fullStr | Remaining useful life prediction: from statistical modeling to federated approach |
| title_full_unstemmed | Remaining useful life prediction: from statistical modeling to federated approach |
| title_short | Remaining useful life prediction: from statistical modeling to federated approach |
| title_sort | remaining useful life prediction from statistical modeling to federated approach |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/156410 |
| work_keys_str_mv | AT ganxin remainingusefullifepredictionfromstatisticalmodelingtofederatedapproach |