Transfer learning applications for anomaly detection in wind turbines

• URL: http://arxiv.org/abs/2404.03011v1
• Date: Wed, 3 Apr 2024 18:48:45 GMT
• Title: Transfer learning applications for anomaly detection in wind turbines
• Authors: Cyriana M. A. Roelofs, Christian Gück, Stefan Faulstich,
• Abstract summary: Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. This study examines how cross-turbine transfer learning can be applied to autoencoder-based anomaly detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. However, training autoencoder models for each turbine is time-consuming and resource intensive. Thus, transfer learning becomes essential for wind turbines with limited data or applications with limited computational resources. This study examines how cross-turbine transfer learning can be applied to autoencoder-based anomaly detection. Here, autoencoders are combined with constant thresholds for the reconstruction error to determine if input data contains an anomaly. The models are initially trained on one year's worth of data from one or more source wind turbines. They are then fine-tuned using smaller amounts of data from another turbine. Three methods for fine-tuning are investigated: adjusting the entire autoencoder, only the decoder, or only the threshold of the model. The performance of the transfer learning models is compared to baseline models that were trained on one year's worth of data from the target wind turbine. The results of the tests conducted in this study indicate that models trained on data of multiple wind turbines do not improve the anomaly detection capability compared to models trained on data of one source wind turbine. In addition, modifying the model's threshold can lead to comparable or even superior performance compared to the baseline, whereas fine-tuning the decoder or autoencoder further enhances the models' performance.

Related papers

• Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis [63.66763657191476]
  We show that efficient numerical training and inference algorithms as low-rank computation have impressive performance for learning Transformer-based adaption. We analyze how magnitude-based models affect generalization while improving adaption. We conclude that proper magnitude-based has a slight on the testing performance.
  arXiv  Detail & Related papers  (2024-06-24T23:00:58Z)
• Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification [2.126171264016785]
  We use physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm. The developed models for regression of the power, torque, and power coefficient showed great accuracy for both real data and physical equations governing the system.
  arXiv  Detail & Related papers  (2023-07-27T07:58:38Z)
• An XAI framework for robust and transparent data-driven wind turbine power curve models [0.8547032097715571]
  Wind turbine power curve models translate ambient conditions into turbine power output. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. We introduce an explainable artificial intelligence framework to investigate and validate strategies learned by data-driven power curve models.
  arXiv  Detail & Related papers  (2023-04-19T17:37:58Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Modeling Wind Turbine Performance and Wake Interactions with Machine Learning [0.0]
  Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm. ML methods for data quality control and pre-processing are applied to the data set under investigation. A hybrid model is found to achieve high accuracy for modeling wind turbine power capture.
  arXiv  Detail & Related papers  (2022-12-02T23:07:05Z)
• Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders [0.0]
  Ice accumulation in the blades of wind turbines can cause them to describe anomalous rotations or no rotations at all. Our approach focuses on two main parts: first, learning low-dimensional representations of time series using a Variational Recurrent Autoencoder (VRAE), and second, using unsupervised clustering algorithms to classify the learned representations as normal (no ice accumulated) or abnormal (ice accumulated)
  arXiv  Detail & Related papers  (2021-12-05T03:41:59Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Efficient Vision Transformers via Fine-Grained Manifold Distillation [96.50513363752836]
  Vision transformer architectures have shown extraordinary performance on many computer vision tasks. Although the network performance is boosted, transformers are often required more computational resources. We propose to excavate useful information from the teacher transformer through the relationship between images and the divided patches.
  arXiv  Detail & Related papers  (2021-07-03T08:28:34Z)
• Online learning of windmill time series using Long Short-term Cognitive Networks [58.675240242609064]
  The amount of data generated on windmill farms makes online learning the most viable strategy to follow. We use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. Our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit, and a Hidden Markov Model.
  arXiv  Detail & Related papers  (2021-07-01T13:13:24Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)
• Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data [0.0]
  This paper explores the possibility of using convolutional neural networks (CNNs), generative adversarial networks (GANs) and domain adaption learning to establish intelligent diagnosis frameworks. We consider a two-stage training with parallel GANs, which are aimed at capturing intrinsic features for normal and icing samples. Model validation on three wind turbine SCADA data shows that two-stage training can effectively improve the model performance.
  arXiv  Detail & Related papers  (2021-01-20T00:46:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.