Digital twin-assisted enhanced meta-transfer learning for rolling bearing fault diagnosis

Ma, Leiming; Jiang, Bin; Xiao, Lingfei; Lu, Ningyun

doi:10.1016/j.ymssp.2023.110490

Cited by 36 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of meta-learning in the field of rotating machinery fault diagnosis has only gained momentum in recent years but has already yielded substantial research outcomes [148,149]. The application of meta-learning primarily addresses the issues of small-sample problems and transfer learning, providing new solutions to the challenges of insufficient fault samples and the time-varying nature of conditions in practical rotating machinery fault diagnosis [150]. Zhao et al [151] considered the problem of negative transfer that traditional meta-learning methods face when updating gradients, proposed an anti-interference integrated metalearning network.…”

Section: Meta Learningmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Deep learning has attained remarkable achievements in diagnosing faults for rotary machineries. Capitalizing on the formidable learning capacity of deep learning, it has the potential to automate human labor and augment the efficiency of fault diagnosis in rotary machinery. These advantages have engendered escalating interest over the past decade. Although recent reviews of the literature have encapsulated the utilization of deep learning in diagnosing faults in rotating machinery, they no longer encompass the introduction of novel methodologies and emerging directions as deep learning methodologies continually evolve. Moreover, in practical application, novel issues and trajectories perpetually manifest, demanding a comprehensive exegesis. To rectify this lacuna, this article amalgamates current research trends and avant-garde methodologies while systematizing the utilization of anterior deep learning techniques. The evolution and extant status of deep learning in diagnosing faults for rotary machinery were delineated, with the intent of providing orientation for prospective research. Over the bygone decade, archetypal deep learning theory has empowered the diagnosis of faults in rotating machinery by directly establishing the nexus between mechanical data and fault conditions. In recent years, meta learning methods aimed at solving small sample scenarios and large model transformers aimed at mining big data features have both received widespread attention and development in the field of fault diagnosis of rotating machinery equipment. Although excellent results have been achieved in these two directions, there is no review and summary article yet, so it is necessary to update the review literature in the field of fault diagnosis of rotating machinery equipment. Lastly, predicated on a survey of the literature and the current developmental landscape, the challenges and prospective orientations of deep learning in rotary machinery fault diagnosis are presented.

show abstract

Section: Meta Learningmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Zhang et al [22] introduced a digital twin-driven intelligent fault diagnosis method for rolling bearings, achieving efficient data generation through a high-fidelity digital twin model to improve data quality and enhance diagnostic accuracy. Ma et al [23] presented a digital twin-assisted enhanced transfer learning method, achieving fault state data generation by simulating typical operating conditions, resulting in less than 5% model error. However, traditional digital twin approaches are often constrained by specific conditions and parameter settings, leading to imbalanced and incomplete datasets.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Liu,

Qi,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, Deep Digital Twins with Information Gain Stacking Sparse Autoencoders(DDT-IGSSAE).Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes Information Gain Ratio (IGR) to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a Stacked Sparse Autoencoder (SSAE) fault classification network is designed, incorporating multi-level feature compression to enhance the model's stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method's effectiveness with a diagnostic accuracy of 98.4%.

show abstract

“…Transfer learning becomes essential to address this challenge. [35][36][37] In response to these challenges, this article proposes a method for multi-location fault detection in rotating machines. It utilizes piezoelectric array sensors and multi-task CNN learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu,

Lo,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

View full text Add to dashboard Cite

This article presents an innovative method for monitoring rotating instruments using piezoelectric array sensors and multi-task CNN (convolutional neural network) learning. The setup involves connecting piezoelectric patches, enabling spatial condition monitoring with a single voltage signal. The sensor array simultaneously tracks four bearing conditions and three gear conditions. However, a single-task CNN faces challenges, especially when trying to distinguish weak gear faults while simultaneously considering different bearing conditions. To address this, the article employs multi-task learning, simplifying the classification task by utilizing shared convolutional layers and two distinct fully connected layers dedicated to gear and bearing health states. This approach is also adaptable for extending fault detection to additional locations. Experiment demonstrates that multi-task learning achieved a 90% accuracy in identifying missing tooth defects in gears, outperforming the 78% accuracy of single-task learning. It confirms multi-task learning's effectiveness in detecting weak faults during multi-location defect monitoring using piezoelectric arrays.

show abstract

Digital twin-assisted enhanced meta-transfer learning for rolling bearing fault diagnosis

Cited by 36 publications

References 28 publications

Application of deep learning to fault diagnosis of rotating machineries

Application of deep learning to fault diagnosis of rotating machineries

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Contact Info

Product

Resources

About