Improving Resilience in Cyber-Physical Systems based on Transfer Learning

Azari, Mehdi; Flammini, Francesco; Santini, Stefania

doi:10.1109/csr54599.2022.9850282

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…Indeed, fault injection and run-to-failure experiments are constrained in real industrial applications for safety, security, and cost concerns. As a result, if PdM models are trained on limited or unbalanced datasets, they can easily overfit, making generalization to the test data difficult [4]. Finally, typically traditional ML and DL algorithms have acceptable performance under the condition that test data comes from the same distribution as training data.…”

Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

“…However, owing to the same operating principle and failure mechanism, the data from the target machine and the data from the source machines should share the same fault characteristic information. [4]. These facts suggest that it may be useful to transfer knowledge across multiple related machines.…”

Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

“…Regarding application objects, predictive maintenance systems based on TL techniques could be used to provide safe and reliable operations of assets within critical systems, e.g., oil and gas, mining, aviation, industrial manufacturing components, and power plants. The feasible transfer scenarios in this context can be divided into the following scenarios [4] (see also Table. 4):…”

Section: A Taxonomy Of Tl In the Field Of Pdm (Sq1)mentioning

confidence: 99%

“…The main weakness of these studies is that only TL within a machine is investigated and only transferred the maintenance knowledge from one operating condition to the next. Whereas, in the real world, the transfer of maintenance knowledge from the data collected from different related machines is the most realistic scenario for the PdM [4]. Moreover, within this challenging scenario, additional reasons, including fault machine specifications, characteristics, etc., can impact the discrepancy of the domains' data distribution in addition to the working condition.…”

Section: ) Application Categorizationmentioning

confidence: 99%

“…Furthermore, unlike traditional ML and DL methods, TL can be successfully applied even if the data distribution of the source and target domains differ. In addition, the amount of computing power and computation time required to train a model can be drastically decreased by leveraging pre-learned knowledge from various source domains [4]. Since TL methods can be effectively exploited for overcoming issues related to traditional ML and DL approaches, in this work we bridge the gap in the technical literature by providing the reference taxonomy, the state-of-the-art (SOTA), and the challenges arising from applying TL to PdM.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

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Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

Section: A Taxonomy Of Tl In the Field Of Pdm (Sq1)mentioning

confidence: 99%

Section: ) Application Categorizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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