A novel federated deep learning framework for diagnosis of partial discharge in gas-insulated switchgear

Self Cite

Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location in gas-insulated switchgear (GIS). However, these methods perform diagnosis and location as two separate tasks and ignore the coupling relationship. In addition, these methods all require obtaining sufficient samples to develop models, and the model becomes ineffective when there is a significant difference in sample distribution. Therefore, we propose a novel domain-alignment multitask learning network (DAMTLN) for condition assessment including diagnosis and location on-site assisted by digital twin. Firstly, a digital virtual model is established to assist the actual condition assessment ofGIS PD. Then, a novel multitask network is constructed to mine the coupling relationship between the two tasks. Finally PD condition assessment guided by a digital twin model are achieved via a combination of local-maximummean-discrepancy-based and adversarial -based domain adaptation, in which fine-grained information on each category is captured. Experimental results show that the proposed DAMTLN achieved a diagnostic accuracy of98.725%, and the mean absolute error of location was 9.055 cm, which were significantly better than the results of other methods. The DAMTLN thus provides a new avenue for PD diagnosis and location driven by “data– physics” coupling..

Section: Accuracy (%) Mae (Cm) Rmse (Cm)mentioning

confidence: 99%

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Yan,

Wang,

Zhang

et al. 2024

Self Cite

“…Recently, data security policies promote the requirement that private data cannot be shared with other users, which poses a challenge for training fault diagnosis models without data aggregation. Federated learning (FL) is developed rapidly in intelligent fault diagnosis [3,4] for providing a privacy protection mechanism to train machine learning models using decentralized data and computing resources. To protect * Authors to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Federated domain generalization with global robust model aggregation strategy for bearing fault diagnosis

Cong

Yan

et al. 2023

Federated learning ensures the privacy of fault diagnosis by maintaining a decentralized and local training data approach, eliminating the need to share confidential information with a central server. However, the performance of trained models tends to significantly deteriorate when applied to completely unseen domains. To address this challenge, this paper introduces a novel method called federated domain generalization with global robust model aggregation strategy. Our proposed method can collaboratively train a model with outstanding generalization ability and robustness to unseen target domains in a data-protecting way. Specifically,the maximum mean discrepancy is introduced in the central server to reduce the discrepancy of features from different source clients. Meanwhile, classification loss across source domains is designed as the weights for local model aggregation in the central server. Finally, the experimental results under two different bearing fault datasets show that our method obtains higher classification accuracy than other compared methods, which demonstrates that the proposed method has better generalization ability and is promising in real industrial applications.

“…In federated learning, data owned by each client is not shared, making it difficult to ensure that it meets the independent and identically distributed (IID) assumption. Addressing the challenge of statistical heterogeneity among multiple parties has been a focus of federated learning research [3,4]. As for fault diagnosis, different equipment or equipment fleets' working conditions often vary, resulting in a shift in data distribution that can slow down or even prevent the convergence of federated models.…”

Section: Introductionmentioning

confidence: 99%

Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Song

2023

Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.