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Full Description
Industrial Fault Diagnosis and Remaining Useful Life Prediction: Cross-Domain, Zero-Sample, and Degradation Modeling Methods introduces zero-sample learning methods that enable fault diagnosis and Predict Remaining Useful Life (RUL) without the need for labelled fault data. This is particularly valuable in industrial settings where labelled data is scarce or unavailable. Offers step-by-step guidance on implementing zero-shot learning models using real industrial data, reducing the learning curve for practitioners; includes real-world industrial case studies to demonstrate the application of zero-sample learning techniques in various industries, such as manufacturing, energy, and transportation. Such case studies provide readers with actionable insights and practical solutions. The book covers advanced methodologies for predicting the remaining useful life of industrial equipment, supporting readers in optimizing maintenance schedules, reducing downtime and extending the lifespan of critical assets. Covers state-of-the-art algorithms, including deep learning, transfer learning and domain adaptation, tailored for zero-sample scenarios. These tools empower readers to develop robust fault diagnosis and RUL prediction systems, enhancing predictive maintenance capabilities and ensuring the reliability of industrial systems.
Contents
1. Introduction
2. Basic theories and methods of intelligent fault diagnosis and health prediction
3. Multi-attribute learning framework for zero-sample fault detection in machinery
4. Generalized zero-sample industrial fault diagnosis with domain bias
5. Generalized zero-sample industrial fault diagnosis under cross-domain scenarios
6. Learning across multisource domains for generalized zero-sample industrial fault diagnosis
7. Federated generalized zero-sample industrial fault diagnosis across multisource domains
8. A multi-phase Wiener process-based degradation model with imperfect maintenance activities
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- 電子書籍
- 蒼き炎 ９巻
