On Applying the Prognostic Performance Metrics

NASA Open Data Portal2025-06-14 收录

预测性维护性能评估

剩余使用寿命预测

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资源简介：

Prognostics performance evaluation has gained significant attention in the past few years. *As prognostics technology matures and more sophisticated methods for prognostic...

Remaining Useful Life Prediction Based on Forward Intensity

剩余使用寿命预测

退化分析

Remaining useful life (RUL) prediction is an essential tool for enhancing the reliability and resilience of systems. Most existing RUL prediction methods consider degradation-induced failures, and ass

DataCite Commons2024-12-02 更新460

The University of Melbourne - Extrusion Filter RUL Dataset

剩余使用寿命预测

工业设备故障诊断

This dataset combines 60 run-to-failure experiments of a polymer extrusion filter. Failure is reached when the contaminants have fully clogged the filter (no throughput) or the filter has burst (inrus

Zenodo2026-06-17 更新80

Residual Life and Reliability Assessment of Underground RC sanitary Sewer Pipelines Under Uncertainty

可靠性评估

剩余使用寿命预测

Prioritization of limited funding for pipelines maintenance is a major issue that concerns municipalities nationwide. Conducting a probabilistic assessment can provide a complete characterization of t

NIAID Data Ecosystem50

数据/模型混合驱动剩余使用寿命预测数据集

剩余使用寿命预测

机械退化监测

本数据集收集了IEEE 2012挑战预测数据集中的数据，并对数据进行处理，这些数据来自于旋转机械退化实验平台PRONOSTIA实验采集，该平台能够在恒定或变化的操作条件下加速轴承的退化，同时收集在线监测数据如转速、负载、温度和振动等。数据集包括在3中不同的负载条件下，6个运行到故障的数据集，剩余寿命持续时间从1到7个小时不等，以保证在数据集上进行预测任务的挑战性。实验平台运行负载数据是收集的施加在

国家基础学科公共科学数据中心270

Feature-Based and Modeling Dataset for Lithium-Ion Battery Remaining Useful Life Prediction Across Multiple Public Datasets

锂离子电池健康管理

剩余使用寿命预测

This dataset contains feature representations and machine learning-ready modeling data for lithium-ion battery Remaining Useful Life (RUL) prediction. The dataset is derived from multiple publicly av

Zenodo2026-06-19 更新60

On Applying the Prognostic Performance Metrics

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