Datasets:

james12221
/

RelaySec-FEL

@@ -1,80 +1,328 @@
-# RelaySec-FEL: Relay Secondary-Circuit Fault Element Localization Dataset
-`RelaySec-FEL` is a dataset of measurements and operational records collected from production relay-protection secondary circuits for element-level fault localization. It supports research on ranking fault elements from incomplete, noisy, delayed, and conflicting multi-source evidence.
-## Data provenance
-The dataset consists of real measurements and records from production operation. The records are organized into relational tables and diagnostic cases, with a static diagnostic graph derived from the structure tables. This tabular and graph organization describes data processing; it does not change the production-measured origin of the data.
-## Task
-For each diagnostic window in `fault_cases.csv`, use static secondary-circuit structure and dynamic evidence to rank the fault element. Candidate elements are:
-```text
-Device ∪ Port ∪ PhysicalLink ∪ VirtualCircuit
-```
-The primary model input is the static structure plus sample-level evidence. Ground-truth labels are supplied separately and are restricted to supervised training and evaluation.
 ```text
-static structure + evidence events for one sample -> ranked candidate fault elements
 ```
-## Dataset snapshot
-- Release version: `1.0.0`
-- Diagnostic cases: 20,000
-- Time range: 2021-01-01 to 2025-12-31
-- Package size: about 103 MB
-- Encoding: UTF-8 CSV with a header row; `metadata.json` contains the authoritative table counts and validation summary.
-| Data group | Files | Purpose |
 |---|---|---|
-| Static structure | `stations.csv`, `bays.csv`, `asset_devices.csv`, `asset_ports.csv`, `physical_links.csv`, `logical_functions.csv`, `virtual_circuits.csv`, `scd_relations.csv`, `entity_alignment.csv` | Build the diagnostic graph and align business identifiers with graph nodes. |
-| Diagnostic windows and evidence | `fault_cases.csv`, `event_observations.csv`, `protection_events.csv`, `soe_events.csv`, `communication_records.csv`, `goose_records.csv`, `sampled_value_records.csv`, `waveform_features.csv`, `self_check_records.csv`, `configuration_records.csv`, `normal_status_records.csv`, `inspection_records.csv` | Represent sample-level observations and health priors. |
-| Labels and maintenance verification | `fault_labels.csv`, `defect_workorders.csv` | Supervision, evaluation, and post-hoc verification only. |
-| Derived static graph | `static_graph/base_nodes.csv`, `static_graph/base_edges.csv`, `static_graph/base_triples.tsv` | Ready-to-use graph representation derived from the static-structure tables. |
-## Quick start
-From the repository root, the provided baseline can be run with the dataset's default location:
-```bash
-conda run -n py311 python \
-  code/src/secondary_circuit_fault_diagnosis/fault_localization/evidence_propagation_diagnosis.py
 ```
-The required derived static graph is included in `static_graph/`, so the default command works after cloning this repository.
-To use a copied dataset elsewhere, pass its directory explicitly:
-```bash
-conda run -n py311 python \
-  code/src/secondary_circuit_fault_diagnosis/fault_localization/evidence_propagation_diagnosis.py \
-  --data-dir /path/to/relaysec-fel
 ```
-## Data-use rules
-`fault_labels.csv` and `defect_workorders.csv` contain the target element, fault mode, verification, or maintenance outcome. They must **not** be joined into model-input features. In particular:
-- `fault_cases.csv` provides the prescribed `train` (14,027), `val` (2,948), and `test` (3,025) split.
-- Only records available before the diagnosis window may be used as evidence; for `inspection_records.csv`, use `inspection_time < case_start_time`.
-- Keep label and work-order tables out of inference-time graph construction, normalization, and feature aggregation.
-- Report results by candidate ranking, rather than treating the task as alarm classification.
-The field definitions and graph mapping are documented in [FIELD_DATA_SCHEMA.md](../../code/resources/field_schema/FIELD_DATA_SCHEMA.md). The machine-readable counterpart is [field_data_schema.json](../../code/resources/field_schema/field_data_schema.json).
-## Data quality and limitations
-The production records include missing observations, suspect quality flags, delayed timestamps, repeated events, normal/recovered evidence, and cross-source conflicts. Controlled evidence perturbations applied in robustness experiments are separate from the original production measurements. Results characterize performance on the released cases and evaluation protocol; deployment at other stations and operating conditions requires further evaluation.
-Validation recorded in `metadata.json` reports zero unmatched event targets and zero labels without a diagnostic case. The metadata file is the release manifest for table counts and validation statistics.
 ## Citation
-If you use this dataset, cite the associated manuscript/repository release. Add the final bibliographic reference, DOI, and versioned archive URL here when they are available.
-## License
-No data license is included in the current repository. Before publishing a public release, the maintainer must add an explicit dataset license (for example, a selected Creative Commons license) and replace this notice with the license identifier and link.

+---
+pretty_name: RelaySec-FEL
+license: cc-by-4.0
+language:
+- en
+- zh
+tags:
+- tabular
+- graph
+- relay-protection
+- secondary-circuits
+- fault-localization
+- fault-diagnosis
+- power-systems
+- multi-source-evidence
+- production-measurements
+configs:
+- config_name: fault_cases
+  default: true
+  data_files:
+  - split: data
+    path: fault_cases.csv
+- config_name: asset_devices
+  data_files:
+  - split: data
+    path: asset_devices.csv
+- config_name: asset_ports
+  data_files:
+  - split: data
+    path: asset_ports.csv
+- config_name: bays
+  data_files:
+  - split: data
+    path: bays.csv
+- config_name: communication_records
+  data_files:
+  - split: data
+    path: communication_records.csv
+- config_name: configuration_records
+  data_files:
+  - split: data
+    path: configuration_records.csv
+- config_name: defect_workorders
+  data_files:
+  - split: data
+    path: defect_workorders.csv
+- config_name: entity_alignment
+  data_files:
+  - split: data
+    path: entity_alignment.csv
+- config_name: event_observations
+  data_files:
+  - split: data
+    path: event_observations.csv
+- config_name: fault_labels
+  data_files:
+  - split: data
+    path: fault_labels.csv
+- config_name: goose_records
+  data_files:
+  - split: data
+    path: goose_records.csv
+- config_name: inspection_records
+  data_files:
+  - split: data
+    path: inspection_records.csv
+- config_name: logical_functions
+  data_files:
+  - split: data
+    path: logical_functions.csv
+- config_name: normal_status_records
+  data_files:
+  - split: data
+    path: normal_status_records.csv
+- config_name: physical_links
+  data_files:
+  - split: data
+    path: physical_links.csv
+- config_name: protection_events
+  data_files:
+  - split: data
+    path: protection_events.csv
+- config_name: sampled_value_records
+  data_files:
+  - split: data
+    path: sampled_value_records.csv
+- config_name: scd_relations
+  data_files:
+  - split: data
+    path: scd_relations.csv
+- config_name: self_check_records
+  data_files:
+  - split: data
+    path: self_check_records.csv
+- config_name: soe_events
+  data_files:
+  - split: data
+    path: soe_events.csv
+- config_name: base_edges
+  data_files:
+  - split: data
+    path: static_graph/base_edges.csv
+- config_name: base_nodes
+  data_files:
+  - split: data
+    path: static_graph/base_nodes.csv
+- config_name: base_triples
+  data_files:
+  - split: data
+    path: static_graph/base_triples.tsv
+  sep: "\t"
+- config_name: stations
+  data_files:
+  - split: data
+    path: stations.csv
+- config_name: virtual_circuits
+  data_files:
+  - split: data
+    path: virtual_circuits.csv
+- config_name: waveform_features
+  data_files:
+  - split: data
+    path: waveform_features.csv
+---
+# RelaySec-FEL
+## Relay Secondary-Circuit Fault Element Localization Dataset
+**Production field measurements · 20,000 diagnostic cases · 8 substations · 2021–2025**
+[Versioned archive](https://doi.org/10.5281/zenodo.21349357) · [All-version DOI](https://doi.org/10.5281/zenodo.21349356) · [Browse files](https://huggingface.co/datasets/james12221/RelaySec-FEL/tree/main)
+RelaySec-FEL contains measurements and operational records collected from relay-protection secondary circuits during production operation. It supports **element-level fault localization**: ranking the devices, ports, physical links, and virtual circuits most likely to explain the observations in a diagnostic window.
+The release brings together circuit structure, communication and protection events, inspection records, and fault labels. It is intended for research on graph-based diagnosis, multi-source evidence fusion, candidate ranking, and evidence-chain explanation under incomplete or conflicting observations.
+## Dataset overview
+| Property | Release v1.0.0 |
+|---|---:|
+| Diagnostic cases | 20,000 |
+| Record period | January 2021–December 2025 |
+| Substations / bays | 8 / 128 |
+| Unified event observations | 156,523 |
+| Static graph nodes | 3,944 |
+| SCD structural relations | 7,608 |
+| Stored graph edges, including reverse relations | 15,216 |
+| Fault-element labels | 20,000 |
+| Inspection records | 19,798 |
+| Defect and maintenance work orders | 8,917 |
+| Data format | UTF-8 CSV; graph triples in TSV |
+| License | CC BY 4.0 |
+The static graph comprises **528 devices, 1,536 ports, 768 physical links, 600 virtual circuits, and 512 logical functions**. Logical functions provide structural context; the localization targets are devices, ports, physical links, and virtual circuits.
+Exact table counts and structural validation statistics are provided in [`metadata.json`](https://huggingface.co/datasets/james12221/RelaySec-FEL/blob/main/metadata.json).
+## Data source and organization
+The underlying data are **real measurements and records from production operation**. The release organizes these records into relational tables and diagnostic cases, together with a static graph derived from the circuit-structure tables. Measurement records, derived representations, and supervision tables have distinct roles in the diagnosis workflow.
+The records cover protection events, sequence-of-events (SOE) logs, communication status, GOOSE and sampled-value records, waveform features, device self-checks, configuration checks, normal or recovered states, and inspections. Observations retain source, time, target, quality, and evidence-reliability fields where provided.
+Controlled perturbations introduced for robustness experiments should be reported separately from the original production records.
+## Task definition
+For each case in `fault_cases.csv`, combine the relevant circuit structure and available observations to return a ranked list of candidate fault elements:
 ```text
+Circuit structure + case observations + pre-fault inspection context
+                              ↓
+             Ranked candidate fault elements
+                              ↓
+          Supporting and opposing evidence paths
 ```
+The candidate types are `Device`, `Port`, `PhysicalLink`, and `VirtualCircuit`. An observation target identifies where a symptom was recorded; the reference fault element identifies the failed component. These may differ because secondary circuits couple physical connections with logical communication mappings.
+`fault_labels.csv` supplies the reference element for supervision and evaluation. Labels and maintenance outcomes are kept separate from inference inputs.
+## Files and relationships
+| Data layer | Files | Role |
+|---|---|---|
+| Circuit structure | `stations.csv`, `bays.csv`, `asset_devices.csv`, `asset_ports.csv`, `physical_links.csv`, `logical_functions.csv`, `virtual_circuits.csv`, `scd_relations.csv` | Describe assets, physical connections, logical functions, and communication mappings. |
+| Entity alignment | `entity_alignment.csv` | Map business-table identifiers to graph nodes. |
+| Diagnostic cases | `fault_cases.csv` | Define case identifiers, time windows, station/bay context, and evaluation splits. |
+| Unified evidence | `event_observations.csv` | Provide the common event representation and observation targets. |
+| Source-specific records | `protection_events.csv`, `soe_events.csv`, `communication_records.csv`, `goose_records.csv`, `sampled_value_records.csv`, `waveform_features.csv`, `self_check_records.csv`, `configuration_records.csv`, `normal_status_records.csv` | Preserve event details and measurements from individual record types. |
+| Inspection context | `inspection_records.csv` | Supply health observations available before a case begins. |
+| Supervision and verification | `fault_labels.csv`, `defect_workorders.csv` | Supply fault-element labels and maintenance outcomes for training, evaluation, and post-hoc verification. |
+| Derived static graph | `static_graph/base_nodes.csv`, `static_graph/base_edges.csv`, `static_graph/base_triples.tsv` | Provide node, edge, and triple representations of the circuit structure. |
+### Core fields
+| Table | Key fields | Interpretation |
 |---|---|---|
+| `fault_cases.csv` | `sample_id`, `station_id`, `bay_id`, `case_start_time`, `case_end_time`, `window_start_time`, `window_end_time`, `split` | Case identity, operating context, diagnosis window, and prescribed partition. |
+| `event_observations.csv` | `event_id`, `sample_id`, `event_time`, `ingest_time`, `target_node_id`, `target_node_type`, `evidence_source`, `event_type`, `value_state`, `quality_flag` | Event identity, case membership, timing, target, source, state, and data quality. |
+| `event_observations.csv` | `source_reliability`, `time_consistency`, `conflict_degree`, `credibility` | Evidence attributes available for reliability-aware modeling; these are not fault probabilities. |
+| `entity_alignment.csv` | `node_id`, `node_type`, `field_table`, `primary_key` | Correspondence between graph nodes and business records. |
+| `fault_labels.csv` | `sample_id`, `fault_node_id`, `fault_node_type`, `fault_mode`, `label_source`, `verification_method`, `label_confidence` | Reference fault element and label documentation. |
+| `inspection_records.csv` | `node_id`, `inspection_time`, `health_score`, `status_code`, `defect_level` | Time-stamped inspection context associated with a graph node. |
+Join cases and unified observations through `sample_id`. Match `target_node_id`, `fault_node_id`, and inspection `node_id` to graph `node_id`; use `entity_alignment.csv` for business identifiers. The unified evidence and source-specific tables provide related views of the records and should not be treated as independent evidence without checking their identifiers.
+<details>
+<summary>Complete table inventory and row counts</summary>
+| File | Rows |
+|---|---:|
+| `fault_cases.csv` | 20,000 |
+| `asset_devices.csv` | 528 |
+| `asset_ports.csv` | 1,536 |
+| `bays.csv` | 128 |
+| `communication_records.csv` | 113,441 |
+| `configuration_records.csv` | 10,469 |
+| `defect_workorders.csv` | 8,917 |
+| `entity_alignment.csv` | 3,944 |
+| `event_observations.csv` | 156,523 |
+| `fault_labels.csv` | 20,000 |
+| `goose_records.csv` | 17,051 |
+| `inspection_records.csv` | 19,798 |
+| `logical_functions.csv` | 512 |
+| `normal_status_records.csv` | 4,249 |
+| `physical_links.csv` | 768 |
+| `protection_events.csv` | 6,793 |
+| `sampled_value_records.csv` | 7,133 |
+| `scd_relations.csv` | 7,608 |
+| `self_check_records.csv` | 4,958 |
+| `soe_events.csv` | 3,438 |
+| `static_graph/base_edges.csv` | 15,216 |
+| `static_graph/base_nodes.csv` | 3,944 |
+| `static_graph/base_triples.tsv` | 19,160 |
+| `stations.csv` | 8 |
+| `virtual_circuits.csv` | 600 |
+| `waveform_features.csv` | 7,133 |
+</details>
+## Load the data
+### Hugging Face Datasets
+Each table has its own configuration because the tables have different schemas. The default configuration is `fault_cases`.
+```python
+from datasets import load_dataset
+repo_id = "james12221/RelaySec-FEL"
+cases = load_dataset(repo_id, "fault_cases", split="data")
+events = load_dataset(repo_id, "event_observations", split="data")
+train_cases = cases.filter(lambda row: row["split"] == "train")
+val_cases = cases.filter(lambda row: row["split"] == "val")
+test_cases = cases.filter(lambda row: row["split"] == "test")
 ```
+Here, `data` is the storage split for a complete table. The **benchmark partitions are the values in the `split` column of `fault_cases.csv`**, not the storage split name. Other configurations use the file stem, such as `asset_devices`, `fault_labels`, `base_nodes`, and `base_edges`.
+### pandas
+For direct access to individual CSV tables:
+```python
+import pandas as pd
+base = "https://huggingface.co/datasets/james12221/RelaySec-FEL/resolve/main"
+cases = pd.read_csv(f"{base}/fault_cases.csv")
+events = pd.read_csv(f"{base}/event_observations.csv")
+nodes = pd.read_csv(f"{base}/static_graph/base_nodes.csv")
+case_id = cases.loc[cases["split"].eq("test"), "sample_id"].iloc[0]
+case_events = events.loc[events["sample_id"].eq(case_id)]
 ```
+The repository contains data and graph representations. Model implementations are supplied separately. For reproducible experiments, report the dataset version and cite the versioned archive below.
+## Evaluation protocol
+| Partition | Diagnostic cases | Share |
+|---|---:|---:|
+| Training (`train`) | 14,027 | 70.135% |
+| Validation (`val`) | 2,948 | 14.740% |
+| Test (`test`) | 3,025 | 15.125% |
+Use the supplied case partitions for comparable results. Report **Top-1 accuracy, Hit@k, and mean reciprocal rank (MRR)** over the candidate ranking, together with the candidate-scope rule and evaluation sample count. If MRR is truncated, state the cutoff explicitly. For different partitions, document how they were formed; the supplied case split should not be assumed to establish chronological or unseen-station generalization.
+To prevent information leakage:
+1. Use `fault_labels.csv` for supervision and evaluation, and `defect_workorders.csv` for post-hoc verification. Exclude both from inference features, evidence aggregation, and candidate selection.
+2. Restrict observations to the information available at the diagnosis cutoff. Account for event time and ingestion time when defining online evaluation.
+3. Use an inspection record only when `inspection_time < case_start_time`.
+4. Fit learned preprocessing and model parameters on training data. Do not derive input features from validation/test fault labels or later maintenance outcomes.
+## Data quality and scope
+Production records contain missing observations, suspect quality flags, delayed timestamps, repeated events, normal or recovered states, and cross-source conflicts. These attributes support evaluation of diagnosis methods under imperfect evidence and should be handled explicitly rather than silently discarded.
+The release metadata reports **zero event targets missing from the graph** and **zero labels without a corresponding diagnostic case**. These are structural consistency checks; they do not imply that every record is complete or every label is certain. Source, quality, verification, and confidence fields should be considered when interpreting individual cases.
+Results on the released cases describe performance under the reported evaluation protocol. Generalization to other substations, configuration changes, and operating conditions requires separate evaluation. Candidate rankings and evidence paths support inspection prioritization; they do not, by themselves, establish a physical fault cause.
 ## Citation
+Please cite the versioned dataset archive:
+> Zeus. (2026). *RelaySec-FEL: Relay Secondary-Circuit Fault Element Localization Dataset* [Data set]. Zenodo. https://doi.org/10.5281/zenodo.21349357
+```bibtex
+@dataset{relaysec_fel_2026,
+  author    = {Zeus},
+  title     = {{RelaySec-FEL}: Relay Secondary-Circuit Fault Element Localization Dataset},
+  year      = {2026},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.21349357},
+  url       = {https://doi.org/10.5281/zenodo.21349357}
+}
+```
+The [all-version DOI](https://doi.org/10.5281/zenodo.21349356) resolves to the latest archived release.
+## License and contact
+The dataset is distributed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/), as registered in the [Zenodo archive](https://zenodo.org/records/21349357). Attribute the dataset creator and cite the dataset when using or redistributing it.
+For questions about the tables, evaluation protocol, or data quality, open a thread in the [dataset discussion area](https://huggingface.co/datasets/james12221/RelaySec-FEL/discussions).