relysam is a comprehensive open-source reliability engineering platform with AI/ML enhancements. It provides tools for reliability analysis, failure mode analysis (FMEA), human reliability analysis (HRA), Weibull analysis, fault tree analysis, event tree analysis, predictive maintenance, risk assessment, and safety engineering.

Features include: THERP, HEART, SPAR-H, CREAM, SLIM-MAUD, SHERPA, ATHEANA, JHEDI, MERMOS methodologies; MIL-HDBK-217, NSWC-11 standards; MTBF/MTTR calculations; life data analysis; Monte Carlo simulation; component derating; event tree analysis; fault tree analysis; maintenance optimization; reliability analysis; FMEA assessment; mixed FMEA analysis; risk assessment; root cause analysis; AI reliability assessment; ML models including neural networks, random forest, forecasting, statistical analysis, correlation analysis, pattern recognition, time series analysis, and trend analysis; system management tools; database integrity checks; and unified reporting.

Industries served: aerospace, automotive, electronics, medical devices, energy, manufacturing, telecommunications, marine. Compatible with FreeBSD and powered by Python 3.11.

Keywords: 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

relysam offers comprehensive solutions for reliability engineers, safety analysts, and risk assessors. The platform integrates traditional engineering methodologies with modern AI/ML techniques to provide predictive insights and automated assessments. Built specifically for FreeBSD environments, it supports Python 3.11 and follows industry standards like MIL-HDBK-217F and NSWC-11 for mechanical reliability predictions.

Download relysam from Codeberg, the open-source platform for reliability engineering tools. Available under GPL-3.0 and GFDL-1.3 licenses, relysam is free to use, modify, and distribute. Join the community of reliability engineers using AI-enhanced tools for improved safety and efficiency.

⚙️ relysam - Core Reliability Engineering Platform

A comprehensive open-source platform for reliability engineering with AI/ML enhancements

🏗️ Platform: FreeBSD 🐍 Python: 3.11 🔢 Version: 1.0.0 🔓 Open Source
🌟 View Repository on Codeberg
🛡️ License: GPL-3.0 📄 License: GFDL-1.3

📋 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:
License: GPL-3.0 and GFDL-1.3
Platform: FreeBSD optimized with Python 3.11 compatibility

Version 1.0
FreeBSD
Python 3.11

🚀 Key Features for Reliability Engineers

🔧 Reliability Analysis Tools

📊 Weibull Analysis

Advanced life data analysis with multiple distribution fitting

🏗️ MIL-HDBK-217F

Military reliability prediction standards implementation

⚙️ NSWC-11

Mechanical reliability prediction methodology

⏱️ 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 Analysis of Thermal Hydraulic Events
📋 JHEDI Specialized Joint Human Experimental Data and Information
🔬 SHERPA Second Gen Systematic Human Error Reduction and Prediction Approach
📋 MERMOS Specialized Methods for Human Reliability Assessment

🤖 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
Main Dashboard
relysam main dashboard provides access to all core reliability engineering tools with AI/ML enhancements.
Reliability Analysis Tools
Reliability Analysis Tools
Advanced reliability analysis 3D visualization of aviation Fuel Management System simulation.
Enhanced Life Data Analysis with AI Results
Enhanced Life Data Analysis with AI Results
AI-powered insights and recommendations for Comprehensive life data analysis.
HRA Methodologies Interface
HRA Methodologies Interface
HRA methods comparison - Capability analysis radar chart.
HRA THERP Learn More Modal
HRA THERP Learn More Modal
Learn more modal for detailed information on THERP (Technique for Human Error Rate Prediction) methodology.
AI Models Training Status
AI Models Training Status
AI/ML model training status and performance metrics, with real time monitoring and automatic retraining.
Knowledge Base Interface
Knowledge Base Interface
Unified search and access to reliability engineering best practices, standards, and failure modes.
Reports and Comparisons
Reports and Comparisons
DfR assessment reports comparison in comprehensive reporting infrastructure.
SQLite Database Integrity Analysis
SQLite Database Integrity Analysis
Database integrity monitoring and analysis tools for ensuring data consistency and reliability.

🛠️ Installation

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

📋 System Requirements

  • Operating System: FreeBSD 13.0+
  • 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

1️⃣ Clone the Repository

git clone https://codeberg.org/0ai/relysam.git
cd relysam

2️⃣ Install System Prerequisites

# Install essential build tools
sudo pkg install gcc gmake cmake py311-devel libffi openssl sqlite3 readline xz zlib

# Install core web framework & API infrastructure
sudo pkg install py311-fastapi py311-starlette py311-uvicorn py311-pydantic py311-python-multipart py311-websockets

# Install data science & analytics foundation
sudo pkg install py311-numpy py311-pandas py311-scipy py311-matplotlib py311-scikit-learn

# Install machine learning & AI frameworks
sudo pkg install py311-torch py311-keras py311-transformers py311-scikit-learn py311-joblib

3️⃣ Create Virtual Environment

# Create virtual environment with system site packages
python3.11 -m venv .venv --system-site-packages
source .venv/bin/activate

4️⃣ Install Remaining Dependencies

# Install the 20 packages that need to be in the virtual environment
pip install --no-deps --no-build-isolation \
    absl-py==2.3.1 \
    autograd-gamma==0.5.0 \
    databricks-sdk==0.76.0 \
    markdown==3.10 \
    markupsafe==3.0.3 \
    mlflow==3.8.1 \
    mlflow-skinny==3.8.1 \
    mlflow-tracing==3.8.1 \
    mplcursors==0.7 \
    oauthlib==3.3.1 \
    packaging==24.2 \
    pillow==12.0.0 \
    py-mini-racer==0.6.0 \
    reliability==0.9.0 \
    requests-oauthlib==2.0.0 \
    safety==3.7.0 \
    setuptools==80.9.0 \
    tensorboard==2.14.1 \
    tf-keras==2.15.0 \
    typing-extensions==4.15.0

5️⃣ Initialize Database and AI Models

python scripts/regenerate_all_binaries.py

6️⃣ Start the Application

# Activate the virtual environment
source .venv/bin/activate

# Start the application
python -m app.main

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

💎 Why Choose relysam?

🔧 Comprehensive Toolset Over 50 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:

  • 📦 399+ system packages (py311-*) accessed via --system-site-packages flag
  • 🐍 20 virtual environment packages installed in .venv directory
  • 💾 Single SQLite database (relysam_ai.db) for all data storage
  • ⚡ FastAPI web framework for REST API and web interface
  • 🧠 10 integrated AI models for predictive analytics

🤝 Contributing

We welcome contributions from the reliability engineering community!

To contribute:

  1. 🐙 Fork the repository
  2. SetBranch Create a feature branch
  3. ✏️ Make your changes
  4. 📝 Add documentation for new features
  5. 📤 Submit a pull request

See our CONTRIBUTING.md file for detailed guidelines.

📚 Documentation

📖 User Manual

Complete user guide

🛠️ Developer Manual

Development guidelines

⚙️ Installation Guide

Complete setup instructions

📄 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

Common Questions About Relysam

What is relysam?

relysam is an open-source reliability engineering platform with AI/ML enhancements that provides tools for FMEA, Weibull analysis, HRA, fault trees, predictive maintenance, and risk assessment.

What industries use reliability engineering tools like relysam?

Reliability engineering tools are used in aerospace, automotive, electronics, medical devices, energy, manufacturing, telecommunications, and marine industries.

What HRA (Human Reliability Analysis) methods does relysam support?

relysam supports THERP, HEART, SPAR-H, CREAM, SLIM-MAUD, SHERPA, ATHEANA, JHEDI, and MERMOS methodologies.

Is relysam free and open source?

Yes, relysam is completely free and open source under GPL-3.0 and GFDL-1.3 licenses.