📋 Overview

relysam is a comprehensive relysam is a Core Reliability Engineering platform with AI/ML enhancements designed for engineers and organizations involved in reliability engineering, safety analysis, and risk assessment. This platform combines traditional reliability engineering methodologies with advanced artificial intelligence to provide powerful tools for predictive maintenance, failure analysis, and risk assessment.

Repository: https://codeberg.org/0ai/relysam
License: GPL-3.0 and GFDL-1.3
Platform: FreeBSD optimized with Python 3.11 compatibility

🚀 Key Features for Reliability Engineers

🔧 Reliability Analysis Tools

📊 Weibull Analysis

Advanced life data analysis with multiple distribution fitting

🏗️ MIL-HDBK-217F

Military reliability prediction methodology (for reference calculations, not certification)

⚙️ NSWC-11

Mechanical reliability prediction methodology (for reference calculations)

⏱️ MTBF/MTTR Calculations

Mean Time Between Failures and Mean Time To Repair

📈 Life Data Analysis

Censored and uncensored data analysis

🎲 Monte Carlo Simulation

Probabilistic risk assessment

🌳 Fault Tree Analysis

Top-down failure analysis

🌿 Event Tree Analysis

Sequential accident modeling

⚠️ Failure Mode Analysis

🔍 FMEA

Failure Mode and Effects Analysis: Comprehensive failure mode identification

🔎 Root Cause Analysis

Systematic investigation of failure causes

🔄 Mixed Failure Analysis

Combined failure mode assessment

👤 Human Reliability Analysis (HRA)

Method	Category	Description
🎯 THERP	First Gen	Technique for Human Error Rate Prediction
❤️ HEART	First Gen	Human Error Assessment and Reduction Technique
⚙️ SPAR-H	First Gen	Standardized Plant Analysis Risk - Human
🧠 CREAM	Second Gen	Cognitive Reliability and Error Analysis Method
📏 SLIM-MAUD	First Gen	Success Likelihood Index Method
🌡️ ATHEANA	Second Gen	A Technique for Human Event Analysis
📋 JHEDI	Specialized	Justification of Human Error Data Information
🔬 SHERPA	Second Gen	Systematic Human Error Reduction and Prediction Approach
📋 MERMOS	Specialized	Méthode d'Evaluation de la Réalisation des Missions Opérateur pour la Sûreté

🤖 AI/ML-Powered Features

🧠 10 Integrated AI Models

Anomaly detection, failure prediction, maintenance optimization

🔮 Predictive Analytics

Machine learning-driven reliability forecasts

💡 Intelligent Recommendations

AI-powered improvement suggestions

🔍 Pattern Recognition

Automated failure pattern identification

⚡ Automated Assessments

AI-enhanced reliability evaluations

🏭 Industry Applications

Industry	Applications	Benefits
✈️ Aerospace	Flight safety, avionics reliability	Enhanced safety margins
🚗 Automotive	Vehicle safety systems, component reliability	Improved vehicle safety
🔌 Electronics	Circuit reliability, semiconductor failure analysis	Reduced failure rates
🏥 Medical Devices	Patient safety, device reliability	Higher safety standards
⚡ Energy	Power plant safety, grid reliability	Improved system stability
🏭 Manufacturing	Production equipment reliability	Reduced downtime
📡 Telecommunications	Network reliability, service availability	Better service quality
🚢 Marine	Ship safety systems, navigation reliability	Enhanced maritime safety

📸 Application Screenshots

Main Dashboard
relysam main dashboard provides access to all core reliability engineering tools with AI/ML enhancements.

Reliability Analysis Tools
Advanced reliability analysis 3D visualization of aviation Fuel Management System simulation.

Enhanced Life Data Analysis with AI Results
AI-powered insights and recommendations for Comprehensive life data analysis.

Knowledge Base Interface
Unified search and access to reliability engineering best practices, standards, and failure modes.

HRA Methodologies Interface
HRA methods comparison - Capability analysis radar chart.

HRA THERP Learn More Modal
Learn more modal for detailed information on THERP methodology.

AI Models Training Status
AI/ML model training status and performance metrics.

SQLite Database Integrity Analysis
Database integrity monitoring and analysis tools.

Reports and Comparisons
DfR assessment reports comparison.

DfR Maturity Assessment Results
Comprehensive view of the DfR maturity assessment results.

NSWC-11 RBD & Design Logic
Automated generation of RBD and design logic.

MIL-HDBK-217F N2 RBD & Design Logic
Hierarchical RBD visualization and expert design logic.

Monte Carlo Growth
Reliability growth simulation.

Monte Carlo 1
Sample analysis 1.

Monte Carlo 2
Sample analysis 2.

Monte Carlo 3
Sample analysis 3.

🛠️ Installation

For detailed installation instructions, please refer to our INSTALLATION.md guide.

🚀 Instant Deployment (Virtual Machine)

For a quick start, we provide Golden QEMU Images based on FreeBSD 15.0. These images include the relysam codebase and most system dependencies pre-installed.

⚠️ Important Notes for VM Users:

Post-Boot Setup Required: After launching the VM, you must manually run database and AI model generation scripts inside the ~/relysam directory.
Incomplete AI Stack: tensorflow and keras are not pre-installed due to FreeBSD 15.0 build/link errors.
Native Recommendation: For the full experience with all AI features, we highly recommend a native FreeBSD 15.0 installation.

📦 Get the VM Image on Codeberg for cross-platform usage

📋 System Requirements

Operating System: FreeBSD 13.0+ (also supports Nomad BSD 14.1+)
Python Version: Python 3.11 (default Python on FreeBSD 15.0)
Permissions: Root access (sudo privileges) to install system packages
Storage: Approximately 4GB free disk space for installation
Memory: 8GB RAM recommended for AI model processing

⚙️ Installation Steps

For a complete, step-by-step installation guide, including detailed system requirements, package groups, virtual environment setup, AI model generation, and database initialization, please refer to the comprehensive INSTALLATION.md.

Access the web interface at: http://localhost:8000
Default accounts: admin/admin123, guest/guest123

Note: In development mode, passwordless authentication is enabled. For production security settings, including configuration file details, see INSTALLATION.md and docs/production_mode_configuration_guide.md.

Recommended: Use the automated installation script: sudo ./install.sh

This script handles all steps automatically, including an optional prompt to import full reference data (approx. 163 rows, see `data/seed/seed_manifest.json`).

For detailed installation procedures, see INSTALLATION.md.

💎 Why Choose relysam?

Note: All currency values throughout this application are in ₹ (Indian Rupee) only.

🔧 Comprehensive Toolset	30+ specialized reliability engineering tools in one platform
🤖 AI/ML Enhancement	Advanced machine learning models for predictive insights
🔓 Open Source	Fully transparent, auditable, and community-driven development
🏗️ Industry Standards	Implements all major reliability engineering methodologies
⚡ FreeBSD Optimized	Efficient performance on FreeBSD systems
🔐 GPL Licensed	Freedom to use, modify, and distribute
🎓 Academic Friendly	Suitable for research and educational purposes
🌐 Maximum Offline Operation	All core functionality works without internet access

🏗️ Architecture

relysam uses a hybrid approach with FreeBSD system packages and virtual environment packages:

📦 414 system packages (py311-*) accessed via --system-site-packages flag
🐍 12 virtual environment packages installed in .venv directory
💾 Single SQLite database (relysam_ai.db) with 126 tables, 30 views, 7 triggers, 170 indices
⚡ FastAPI web framework for REST API and web interface
🧠 10 integrated AI models for predictive analytics

🧪 Testing & Quality Assurance

relysam ensures robustness through a comprehensive automated test suite:

158 Functional Tests: Including the new Mixed FMEA workflow validation.
Comprehensive Coverage: Fast and slow test categories with coverage reporting.
Pre-commit Integration: Ensures code quality before every commit.
Integration Testing: Validates AI model lifecycle, API endpoints, and database integrity.

See the tests/README.md and docs/AUTOMATED_TEST_SUITE.md for detailed documentation.

🤝 Contributing

We welcome contributions from the reliability engineering community!

To contribute:

🐙 Fork the repository
SetBranch Create a feature branch
✏️ Make your changes
📝 Add documentation for new features
📤 Submit a pull request

See our CONTRIBUTING.md file for detailed guidelines.

📚 Documentation

📄 License

This project is licensed under the GNU General Public License v3.0 (GPL-3.0) and GNU Free Documentation License v1.3 (GFDL-1.3). See the LICENSE_GPL and LICENSE_GFDL files for details.

🆘 Support

❓ Support

Issues & Support

Pull Requests

Wiki

🔍 Keywords for Search

reliability engineering, FMEA, failure mode analysis, human reliability analysis, HRA, THERP, HEART, SPAR-H, CREAM, SLIM-MAUD, SHERPA, ATHEANA, JHEDI, MERMOS, Weibull analysis, MIL-HDBK-217, MTBF, MTTR, fault tree analysis, event tree analysis, predictive maintenance, risk assessment, safety engineering, open source, FreeBSD, machine learning, AI, predictive analytics, life data analysis, Monte Carlo simulation, aerospace safety, automotive reliability, medical device safety, power systems reliability, manufacturing reliability, telecommunications reliability