SKAB - Skoltech Anomaly Benchmark 核心 · 已核验
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SKAB(Skoltech):水循环台架异常检测基准,34 组实验带标注异常区间;振动/压力/温度/流量/电流多通道。数据与代码同仓,仓库许可 GPL-3.0(仓库许可≠数据许可,注意保留)
本卡描述如含来自官方页的原文片段,其版权归原作者,不在本站 CC-BY 4.0 许可范围(见关于与许可)。
- 落地页
- https://github.com/waico/SKAB
- 许可证
- GPL-3.0 (置信:verified_official)
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2020
- 别名
- SKAB
- 设备类型
pump- PHM 任务
anomaly_detection
故障工况
| description: 34 组实验注入异常(阀门操作/不平衡等),带标注异常区间fault_type: other |
传感器
| sensor_type: accelerometerobserved_property: vibration_acceleration |
| sensor_type: pressure_transducerobserved_property: pressure |
| sensor_type: thermocoupleobserved_property: temperature |
| sensor_type: flow_meterobserved_property: flow_rate |
| sensor_type: current_sensorobserved_property: electric_current |
运行工况
| description: 水循环试验台受控实验(阀操作/失衡等 34 组注入)condition_type: other |
关联论文(77 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- Anomaly Detection in Smart Environments: A Comprehensive Survey 2024 · 综述收录
- Timed fuzzy quantum-inspired anomaly detection in industrial scenarios 2026 · 用于方法验证
- Adaptive Statistical Windowing for Anomaly Detection in Industrial Sensor Signals 2026 · 用于方法验证
- Anomaly Detection and Fault Diagnosis Based on Action States for Excavators 2026 · 用于方法验证
- Uncertainty-Aware Temporal Convolutional Networks for Multivariate Anomaly Detection: A Composite-Objective Framework with Chebyshev Bounds 2026 · 用于方法验证
- Nonlinear and Trend-Aware Industrial Time Series Anomaly Detection with Federated Learning 2026 · 用于方法验证
- Prototype-Based Dynamic Autoencoders for Real-Time Anomaly Detection and Explainability 2026 · 用于方法验证
- HDT-AD: Collapse-Resistant Streaming Anomaly Detection in Constant Memory via Hyperdimensional Transforms 2026 · 用于方法验证
- Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems 2026 · 用于方法验证
- A Deep Learning Approach for Time Series Data Correction:Integrating Autoencoder-Based GANs andCorrelation Analysis 2026 · 候选引用(未核验)
- Federated Deep Learning for Telecom-Orchestrated Anomaly Detection in Industrial IoT Critical Infrastructure Networks 2026 · 候选引用(未核验)
- IoT Anomaly Detection Using Picture Fuzzy Clustering Approach 2026 · 候选引用(未核验)
- EcoChainAI: Multi-Facility Industrial Telemetry and Carbon Emission Benchmark Dataset Suite 2026 · 候选引用(未核验)
- EcoChainAI: Multi-Facility Industrial Telemetry and Carbon Emission Benchmark Dataset Suite 2026 · 候选引用(未核验)
- Benchmarking IoT Time-Series AD with Event-Level Augmentations 2026 · 候选引用(未核验)
- RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms 2026 · 候选引用(未核验)
- FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models 2026 · 候选引用(未核验)
- Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection 2026 · 候选引用(未核验)
- Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings 2026 · 候选引用(未核验)
- Weighted Score-Oriented Losses for Temporally Localized Event Prediction 2026 · 候选引用(未核验)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(10 条)
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-09 |
| 日期: 2026-07-11 |
| 日期: 2026-07-11 |
| 日期: 2026-07-14 |
| 日期: 2026-07-21 |
| 日期: 2026-07-25 |
| 日期: 2026-07-26 |