AI-Powered SIF Precursor Detection & Safety Intelligence Platform for Industrial Safety

AI-Powered SIF Precursor Detection & Safety Intelligence Platform for Industrial Safety

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  S M AR T I N D I A H A C K AT H O N 2 0 2 6 AI-Powered SIF Precursor Detection & Safety Intelligence Platform Turning unstructured safety narratives into explainable, actionable risk intelligence PROBLEM STATEMENT ID SIH26165 PROBLEM STATEMENT SIF Precursor Detection ORGANIZATION Oil India Limited PS CATEGORY Software THEME / DOMAIN AI, NLP & Industrial Safety TEAM ID [Team ID Placeholder] TEAM NAME [Team Name Placeholder] Software · AI/NLP · Industrial Safety Oil India Limited | Problem Statement SIH26165
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  I D EA / P R O P O S E D S O LU T I O N AI-Powered SIF Precursor Detection The Problem • Important SIF precursor signals are buried inside large volumes of unstructured safety reports • Manual analysis is time-consuming and inconsistent • Early warning signals may be missed • Safety teams need a faster way to identify high-risk patterns Our Solution 1 Upload / ingest incident, near-miss & unsafe-act reports 2 NLP preprocessing cleans and structures the text 3 AI extracts risk-related entities and signals 4 ML/NLP model identifies SIF precursor patterns 5 System assigns precursor category and risk score 6 Dashboard lets safety officers review, filter and prioritize KEY FEATURES Automated report analysis SIF precursor classification Risk / severity scoring Explainable AI Entity & keyword extraction Trend analysis Search & filtering Safety analytics dashboard Alert & prioritization WHAT MAKES IT DIFFERENT • Converts unstructured narratives into actionable safety intelligence • Targets SIF precursors specifically — not generic incident tagging • Explainable outputs build safety-officer trust Smart India Hackathon 2026 | PS ID: SIH26165 | SIF Precursor Detection 2
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  SYS T EM D E S I G N Technical Approach Safety Reports → Data Ingestion → NLP Preprocessing → Feature / Entity Extraction → SIF Classification → Risk Scoring → Explainable Results → Dashboard & Alerts TECHNOLOGY STACK Frontend React / HTML / CSS / JavaScript Backend Python + FastAPI / Flask NLP Python, spaCy, Transformers ML Scikit-learn, XGBoost, Transformer classifier Database PostgreSQL / MongoDB Visualization Chart.js / Plotly Auth Role-based access control Deployment Cloud / on-prem server AI PIPELINE — STEP BY STEP 1 Collect historical safety reports 2 Clean and normalize text 3 Tokenization & linguistic preprocessing 4 Extract entities: equipment, activity, hazard, location, unsafe condition 5 Detect SIF precursor indicators 6 Classify report into precursor categories 7 Generate a risk score 8 Explain key detected signals 9 Display results on the dashboard Smart India Hackathon 2026 | PS ID: SIH26165 | SIF Precursor Detection 3
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  G R OU N D E D A S S E S S M E N T Feasibility & Viability Technical Feasibility • Uses established NLP and machine-learning technologies • Can work with existing digital safety reports • Can initially operate with historical labeled datasets • Deployable on existing enterprise infrastructure Challenges • Limited labeled SIF precursor datasets • Different reporting styles & terminology • False positives / false negatives • Data privacy and confidentiality • Model explainability • Domain-specific terminology Mitigation Strategies • Human-in-the-loop validation • Domain-specific NLP vocabulary • Continuous model retraining • Confidence thresholds • Explainable AI techniques • Role-based access & encryption • Audit logs & performance monitoring Scalability: The system is designed to support increasing numbers of reports, departments, locations and users as adoption grows. Smart India Hackathon 2026 | PS ID: SIH26165 | SIF Precursor Detection 4
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  VA LU ED E L I V E R E D Impact & Benefits Who Benefits Safety officers HSE teams Incident investigators Plant / site managers Management Industrial safety analysts Early identification of SIF precursors Faster analysis of safety reports Reduced manual workload Improved prioritization of high-risk incidents Identification of recurring hazards and trends Data-driven safety decision making Proactive rather than reactive safety management Improved workplace safety culture Unstructured Reports → AI Analysis → Risk Signals → Safety Action → SIF Prevention Smart India Hackathon 2026 | PS ID: SIH26165 | SIF Precursor Detection 5
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  G R OU N D E D I N C R E D I B L E S O U R C E S Research & References Industry & Organization Oil India Limited — Corporate HSE & Safety https://www.oil-india.com IOGP — Process Safety Resources https://www.iogp.org/workstreams/safety/safety/process-safety/ IOGP — Safety Performance Indicators & Reporting https://www.iogp.org/bookstore/product-category/safety/ Standards & Guidance OSHA — Injury & Illness Recordkeeping / RCA Guidance https://www.osha.gov Official Smart India Hackathon Documentation https://www.sih.gov.in API RP 754 — Process Safety Performance Indicators (referenced via IOGP) https://www.iogp.org Research Literature Parikh, Penfield & Juaire — "Automatic identification of incidents involving potential serious injuries and fatalities (PSIF)", Scientific Reports, 2024 https://doi.org/10.1038/s41598-024-58824-y Peer-reviewed research on incident-text mining & NLP for occupational safety (Scientific Reports / PMC) https://www.ncbi.nlm.nih.gov/pmc AI / NLP Documentation spaCy — Industrial-strength NLP documentation https://spacy.io Hugging Face Transformers — Model & pipeline documentation https://huggingface.co/docs/transformers Scikit-learn — Machine learning library documentation https://scikit-learn.org Smart India Hackathon 2026 | PS ID: SIH26165 | SIF Precursor Detection 6