open-fdd is an open-source Fault Detection and Diagnostics (FDD) tool designed for analysts and engineers using local toolsets like Jupyter notebooks. It is not necessarily an IoT tool for Grafana, which an MSI (Master Systems Integrator) might use, though it could be adapted for that purpose. Instead, it is tailored for individual engineers analyzing historical HVAC system data using the Pandas computing library. While it could potentially be integrated with a database, doing so may require additional effort. It leverages ASHRAE and NIST-inspired fault equations. Built on Python and Pandas, this library enables efficient detection of operational issues in HVAC systems with:
This version improves clarity and flow while keeping it professional and readable. π Let me know if you want any more refinements!
β Pre-built fault equations for detecting HVAC anomalies β Seamless Pandas integration for time-series analysis β Extensible architecture for custom fault conditions β Open-source & community-driven development
π See Online Documentation:
π Open-FDD Docs
Install open-fdd from PyPI with:
pip install open-fddimport pandas as pd
from open_fdd.air_handling_unit.fault_condition_one import FaultConditionOne
# Sample data
data = {
"timestamp": pd.date_range(start="2023-01-01", periods=10, freq="15T"),
"supply_air_temp": [54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
"return_air_temp": [70, 70, 70, 70, 70, 70, 70, 70, 70, 70],
}
df = pd.DataFrame(data)
# Run fault detection
fault_checker = FaultConditionOne(df)
df_faults = fault_checker.process()
print(df_faults)open-fdd aims to provide a full-featured Fault Detection & Diagnostics (FDD) platform with:
- Air handling unit (AHU) fault conditions & reports (aligned with ASHRAE/NIST)
- PyPI distribution for easy installation
- Jupyter notebook tutorials showcasing AHU FDD examples + BRICK metadata integration
- Expansion to central plant fault conditions (chillers, boilers, pumps)
- Jupyter notebook tutorials showcasing AHU FDD examples + BRICK metadata integration
- Energy Efficiency fault detection & reporting
- Metering fault analytics & data modeling
- SQL Integration for storing results & visualizing in Grafana
- Dedicated documentation site (
github.ioor ReadTheDocs)
We welcome contributions from the community! To get started:
- Clone the repository:
git clone https://github.com/bbartling/open-fdd.git && cd open-fdd- Install dependencies:
py -3.12 -m pip install -r requirements.txt- Run tests:
py -3.12 -m pytest- Format with Black:
py -3.12 -m black .- Submit a Pull Request (PR)
open-fdd is released under the MIT License, ensuring it remains free and accessible for all.
γMIT Licenseγ
Copyright 2024 Ben Bartling
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.