This thesis leverages vibration-based unsupervised learning and deep transfer learning to reduce the manual labour involved in building algorithms that perform intelligent fault detection (IFD) on roller element bearings. A review of theory and literature in the field of IFD is presented, and challenges are discussed. An issue is then introduced; current machine learning models built for IFD show strong performance on a small subset of specific data, but do not generalize to a broader range of applications. Signal processing, machine learning, and transfer learning concepts are then explained and discussed. Time-frequency fingerprinting, as well as feature engineering, is used in conjunction with principal component analysis (PCA) to prepare vibration signals to be clustered by a gaussian mixture model (GMM). This process allows for the intelligent referral of data towards algorithms that have performed well on similar datasets and favours the re-use of domain-specific tasks. An algorithm is then proposed that promotes generalization in convolutional neural networks (CNNs) and simplifies the hyperparameter tuning process to allow machine learning models to be applied to a broader set of problems. The machine learning process is then automated as much as possible through meta learning and ensemble models: data similarity measurements are used to evaluate the data fit for transfer and propose training guidelines. Throughout the thesis, three open-source bearing fault datasets are used to test and validate the hypotheses. This thesis focuses on developing and adapting current deep learning models to succeed in challenging domains and real-world scenarios, while improving performance with unsupervised learning and transfer learning.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45896 |
| Date | 29 January 2024 |
| Creators | Larocque-Villiers, Justin |
| Contributors | Dumond, Patrick, Knox, David |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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