shambhuraje/Indian_Railway_maintance

license: cc-by-4.0 task_categories:

• —tabular-classification
• —tabular-regression
• —time-series language:
• —en tags:
• —railway
• —predictive-maintenance
• —failure-detection
• —transportation
• —iot
• —machine-learning
• —synthetic-data
• —analytics prettyname: Indian Railway Failure Detection & Maintenance (100K) sizecategories:
• —100K<n<1M ---

🚆 Indian Railway Failure Detection & Maintenance (100K)

Overview

This dataset contains 100,000 synthetic yet realistic railway maintenance records designed for predictive maintenance, failure detection, and transportation analytics. The data simulates real-world railway operations through equipment wear, maintenance history, environmental conditions, operational metrics, and IoT-inspired sensor readings.

Version 2 incorporates more realistic feature relationships, seasonal weather patterns, equipment aging effects, structured missing values, and sensor outliers to better reflect operational railway environments.

Features

The dataset includes information related to:

• —🚄 Train operations
• —⚙️ Equipment health indicators
• —📡 IoT sensor measurements
• —🌦️ Weather and environmental conditions
• —🛤️ Track health metrics
• —🔋 Electrical system indicators
• —🔧 Maintenance history
• —⚠️ Failure types and severity levels
• —📊 Inspection and risk scores

Target Variables

Maintenance Required

Binary target:

• —0 = No Maintenance Required
• —1 = Maintenance Required

Failure Type

Possible values:

• —None
• —Brake Failure
• —Wheel Defect
• —Track Defect
• —Signal Failure
• —Bearing Failure

Failure Severity

Possible values:

• —None
• —Low
• —Medium
• —High
• —Critical

Machine Learning Applications

This dataset can be used for:

• —Predictive Maintenance
• —Failure Detection
• —Multiclass Classification
• —Risk Assessment
• —Anomaly Detection
• —Data Imputation
• —Regression Tasks
• —Feature Engineering
• —Explainable AI (XAI)

Data Characteristics

• —📈 Records: 100,000
• —📊 Features: 25+
• —🧩 Realistic Missing Values
• —📡 Sensor Outliers Included
• —🌦️ Seasonal Weather Effects
• —⚙️ Correlated Equipment Wear
• —🚄 Multiple Train Categories
• —🎯 Multiple Prediction Targets

Synthetic Data Notice

This dataset is synthetically generated by the author and does not contain real railway operational records.

Although synthetic, realistic relationships, operational patterns, maintenance logic, missing values, and failure mechanisms have been incorporated to emulate real-world predictive maintenance scenarios.

Example Use Cases

• —Railway Failure Prediction
• —Predictive Maintenance Systems
• —Industrial IoT Analytics
• —Transportation Analytics
• —Educational Projects
• —Data Science Portfolios
• —Kaggle Competitions & Notebooks

License

This dataset is released under the CC BY 4.0 License.

Users are free to share and adapt the dataset with appropriate attribution.

Citation

If you use this dataset in research, projects, notebooks, or publications, please provide attribution to the dataset author.

🚆 Built for machine learning, analytics, predictive maintenance, and transportation research. ---