a general library for fatigue and reliability
fatpack provides functions and classes for fatigue analysis of data series.
Fast rainflow counting written in C (C99). Including wrappers for MATLAB and Python
Calculate and optimise fatigue crack growth
A C++ implementation of the ASTM E1049-85 rain flow cycle counting algorithm for fatigue analysis
This python script converts any speed/torque (or current/voltage) profile of an electric drive into thermal power cycles, and then quantifies the lifetime damage produced by that profile.
Rainflow‑aware DQN for battery dispatch (arbitrage + FR/reserves): Gymnasium env with safety layer, exact cycle‑based degradation, time features & stacked observations; SB3 training.
This QuickApp predicts the rain in Europe with data from the Buienradar, two hours in advance
Measurement analysis for mechanical test engineers: messy CSV/TDMS/MDF4 in, vibration/fatigue/thermal answers out — CLI, HTML reports, and an MCP server for LLM-driven analysis.
AI-agent-native low cycle fatigue strain-life analysis. Python library plus MCP server: reduce strain-controlled test data, fit Basquin, Coffin-Manson, and Ramberg-Osgood models, predict life with mean-stress corrections, and save results for recall.
Grid battery state of charge from market data (validated on operator SoC), LFP ageing model fitted to cell data, and degradation-aware arbitrage in Australia's NEM
Grid battery operations against warranty and LTSA: metered RTE with auxiliaries, contractual availability, cycles and warranty violations, capacity tests and SoH forecast; monthly Excel/HTML report; benchmarked on every NEM battery (AEMO) and validated with operator data.
Key performance indicators for operating PV, wind and battery assets: PR, availability, energy bridge, power curves, reliability, RTE, cycles, capacity tests and contract damages
Wind turbine loads from SCADA data: detection of edgewise blade instabilities and their fatigue cost in a simulated 12-turbine fleet, and a machine-learning load emulator for power de-rating, built on a reduced-order aero-servo-elastic model and checked against real 20 Hz measurements of the Björkö 45 kW research turbine.
Fast C++ implementations of fatigue cycle-counting algorithms with Python bindings.
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