# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance

arXiv: https://arxiv.org/abs/2403.00975 · HTML: https://mlsanity.com/paper/2403.00975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Non-linear Functional Modeling using Neural Networks](https://mlsanity.com/paper/2104.09371/) (2021)
- [Modern Non-Linear Function-on-Function Regression](https://mlsanity.com/paper/2107.14151/) (2021)
- [A Non-linear Function-on-Function Model for Regression with Time Series Data](https://mlsanity.com/paper/2011.12378/) (2020)
- [RaSE: Random Subspace Ensemble Classification](https://mlsanity.com/paper/2006.08855/) (2020)
- [An ensemble of convolution-based methods for fault detection using vibration signals](https://mlsanity.com/paper/2305.05532/) (2023)

## Built on by (most cited first, 1 of 1)

- [Early wind turbine alarm prediction based on machine learning: AlarmForecasting](https://mlsanity.com/paper/2510.06831/) (2025)

## Abstract (arXiv)

In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts.