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A matched-protocol comparison of synchronous ring gossip, FedAvg, local and centralized training for LSTM failure detectors on the NASA C-MAPSS turbofan benchmark, showing gossip as a practical serverless alternative to federated averaging that matches FedAvg performance without a coordinator.
The paper proposes a Multi-Term Fourier Graph Neural Network with Sample Relationship Learning (MTFGN-SRL) to enhance remaining useful life prediction by using frequency domain analysis and learning inter-sample relationships, addressing limitations in current spatio-temporal graph neural networks.
This paper introduces a synthetic multivariate time-series dataset for refrigerator predictive maintenance, generated using a physics-inspired simulator to support failure prediction and degradation analysis.
This paper presents a predictive maintenance framework that uses deep learning and uncertainty estimation to improve remaining useful life estimation for semiconductor manufacturing, reducing maintenance costs.
FedCMAPSS introduces a benchmark for federated learning in remaining useful life estimation based on the NASA C-MAPSS dataset, with standardized tasks to evaluate federated optimization algorithms across neural architectures.
This paper introduces an optimized fuzzy logic approach combined with the IEEE Key Gas Method for diagnosing power transformer faults using dissolved gas analysis, achieving up to 98.6% accuracy in experimental validation.
This paper proposes a transferable autologistic model for predicting rare equipment failures across heterogeneous sensor configurations, evaluating it on a synthetic refrigerator dataset.
This paper demonstrates that naive train/test splitting on sliding-window sequences can severely inflate or deflate performance metrics in multi-task learning for predictive maintenance, and proposes a leakage-robust evaluation protocol.
Proposes a knowledge-guided two-stage transfer learning framework using a lightweight GPT-2-style Transformer for cross-domain bearing fault diagnosis with limited data, achieving 92.61% accuracy with only 10% labeled data.
This paper proposes a quantum annealing enhanced Q-learning framework for remaining useful life prediction, using the D-Wave system to solve QUBO formulations for action selection. It outperforms classical and quantum baselines on NASA C-MAPSS and predictive maintenance datasets.
This paper introduces a lightweight approach for remaining useful life estimation using frozen embeddings from the Chronos-2 time-series foundation model combined with a simple regression head, achieving superior performance on industrial sensor data compared to baseline methods.
This paper proposes a semantic feature segmentation framework for predictive maintenance that decomposes monitoring signals into canonical and residual components to improve interpretability while maintaining predictive performance.
This paper introduces HEPA, a self-supervised architecture for predicting rare critical events in time series using a Joint-Embedding Predictive Architecture (JEPA) pretraining strategy. It demonstrates superior performance across multiple domains with significantly fewer labeled data and tuned parameters compared to leading models.