C-MAPSS
PulseAugur coverage of C-MAPSS — every cluster mentioning C-MAPSS across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Time-series retrieval boosts MLLM accuracy in predicting machinery lifespan
Researchers have developed a new framework that uses time-series retrieval to improve the accuracy of multimodal large language models (MLLMs) in predicting remaining useful life (RUL) for machinery. This approach invol…
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Federated learning advances aircraft engine prognostics with robust personalization
Researchers have developed a federated learning approach to train aircraft engine prognostics models while addressing both benign and adversarial data heterogeneity. The study utilized a multi-task one-dimensional convo…
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New CruiseBench benchmark standardizes aircraft engine RUL prediction
Researchers have introduced CruiseBench, a new benchmark designed to standardize the evaluation of remaining useful life (RUL) prediction models for aircraft engines. This benchmark is derived from the N-CMAPSS dataset,…
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New framework provides counterfactual explanations for AI in predictive maintenance
Researchers have developed SurvCF(t), a novel framework designed to provide counterfactual explanations for survival models used in predictive maintenance with multivariate time-series data. This system identifies the s…
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New liquid neural network models turbofan engine degradation
Researchers have developed a new liquid neural network model for predicting turbofan engine degradation. This model aims to provide a more interpretable view of an aircraft engine's health by separating degradation from…
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New AI framework enhances equipment health prediction accuracy
Researchers have developed a new framework called Reinforced Graph-based Physics-informed Networks with Dynamic Weighting (RGPD) to improve the accuracy of Remaining Useful Life (RUL) and State of Health (SoH) estimatio…
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Machine learning models compared for turbofan engine remaining useful life estimation
A new research paper compares classical machine learning methods, 1D Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks for estimating the remaining useful life of turbofan engines. The stu…