Extend Python AI for Additional Anomaly Detection
Budget: $750 – $1,500 USD
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.
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.