Title Extend & Refactor Existing Python AI Diagnostic System Offline Time-Series Analytics – Reuse Existing GitHub Project Description We have an existing Python-based diagnostic and AI analytics system on GitHub. The current implementation successfully detects one anomaly type (e.g. valve leakage) using time-series machine data. We are now looking for a developer to reuse, refactor, and extend this existing system to support additional anomaly types, while keeping the software offline, headless, and reusable. Objective The software will: Process exported time-series data (batch / offline) Reuse the existing analytics and AI logic Extend detection to additional anomaly categories Output simple, explainable diagnostic results for engineering use The same analytics core will later be reused in other projects, so clean separation from UI and data acquisition is mandatory. Budget Single delivery, offline prototype IMPORTANT – READ CAREFULLY A working GitHub repository already exists and will be provided This task includes reviewing, reusing, and extending existing code It is NOT a rewrite It is NOT open-ended AI research Proposals assuming a rebuild from scratch will be rejected Scope of Work 1. Review Existing Codebase Review the current Python repository Identify and reuse: existing ingestion logic feature extraction AI / anomaly logic Isolate or remove: UI or demo code hard-coded paths non-essential scaffolding 2. Input Normalisation Support exported time-series data files (CSV / XML or similar) Map inputs into a common internal format, for example: timestamp channel / sensor type value unit Batch / offline execution only 3. Feature & Anomaly Extension Reuse existing feature calculations Extend detection beyond valve leakage to additional anomaly types, such as: abnormal vibration behaviour instability or variability increase pressure-related anomalies trend-based degradation indicators Anomalies may be implemented using: simple ML extensions or deterministic / rule-based logic where appropriate Focus on engineering relevance and explainability, not model complexity 4. Offline AI / Analytics Execution Extend existing AI logic to support multiple anomaly categories Keep ML additions small and controlled Provide rule-based fallback logic for clarity and robustness 5. Outputs Generate simplified diagnostic outputs such as: normal / warning / fault anomaly category basic confidence indicator Output formats: CSV JSON 6. Execution CLI or script-based execution, e.g.: python run_analysis.py input_folder/ output_folder/ Must run locally on a standard laptop. Deliverables Updated Python code (Python 3.8+) Clean folder structure README explaining: supported input formats anomaly types implemented how to run the analysis Sample input data and example outputs Explicitly Out of Scope Required Skills Python Pandas / NumPy Time-series analysis Practical ML or anomaly detection Experience refactoring existing codebases Nice to Have Engineering or industrial data background Diagnostic / condition-monitoring experience Mandatory Screening Questions Please answer briefly: Have you extended an existing analytics or ML system to support new anomaly types? How would you add new anomaly detection without rewriting the system? Confirm you understand this project is offline only, with no UI.