Automated Hazard Analysis with AI
detail.loadingPreview
Streamline your safety engineering by automating hazard analysis. This n8n workflow leverages AI to identify potential hazards, their root causes, and relevant ISO 26262 clauses from system descriptions.
About This Workflow
This n8n workflow automates the critical process of hazard analysis, significantly reducing manual effort and enhancing accuracy. It begins by reading a system description file and then feeds this data into an AI agent. The AI agent, powered by sophisticated language models like GPT-4, analyzes the provided system description to identify up to five potential hazards, their likely root causes, and the corresponding clauses from the ISO 26262 standard. The results are then consolidated and saved into a report file, ensuring a comprehensive and auditable record of the hazard identification process. This workflow is ideal for organizations seeking to improve their safety engineering practices and ensure compliance with automotive safety standards.
Key Features
- AI-Powered Hazard Identification: Utilizes advanced AI to automatically detect potential hazards.
- Root Cause Analysis: Identifies the probable underlying causes for each hazard.
- ISO 26262 Compliance: Maps identified hazards to relevant ISO 26262 clauses.
- Automated Reporting: Generates a comprehensive report of the analysis.
- Configurable Input: Works with plain text system descriptions.
How To Use
- Trigger Workflow: Manually execute the workflow by clicking 'Execute workflow'.
- Provide System Description: Ensure your system description is saved as 'systems_description.txt' in the
/data/inputs/1_hazard_identification/directory. - AI Analysis: The workflow reads the file, converts it to binary data, and then uses an AI agent with an LM Chat (OpenAI) node to analyze the text.
- Configure AI Model: Ensure the 'AI_Hazard_Analysis' node is configured with your desired OpenAI model (e.g., gpt-4.1-mini) and the correct OpenAI API credentials.
- Memory Configuration: The 'A simple memory window' node is configured to store session data using the execution ID, which helps maintain context for more complex interactions if extended.
- Output Generation: The AI's output is converted to text and saved as 'Report_Hazard Identification.txt' with a timestamp in the
/data/outputs/1_hazard_identification/directory.
Apps Used
Workflow JSON
{
"id": "5552474e-2016-455b-8d82-70cc7808452c",
"name": "Automated Hazard Analysis with AI",
"nodes": 18,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 5552474e-2016...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
Statistics
Related Workflows
Discover more workflows you might like
Effortless Bug Reporting: Slack Slash Command to Linear Issue
Streamline your bug reporting process by instantly creating Linear issues directly from Slack using a simple slash command. This workflow enhances team collaboration by providing immediate feedback and a structured approach to logging defects, saving valuable time for development and QA teams.
Automated PR Merged QA Notifications
Streamline your QA process with this automated workflow that notifies your team upon successful Pull Request merges. Leverage AI and vector stores to enrich notifications and ensure seamless integration into your development pipeline.
Visualize Your n8n Workflows: Interactive Dashboard with Mermaid.js
Gain unparalleled visibility into your n8n automation landscape. This workflow transforms your n8n instance into a dynamic, interactive dashboard, leveraging Mermaid.js to visualize all your workflows in one accessible place.