On damage diagnosis for a wind turbine blade using pattern recognition

Abstract. In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamage and damage states of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

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“…This particular blade has been widely studied [35,36,37]. A simplified finite element model was generated in ANSYS using shell elements.…”

Section: Wind Turbine Blade Modelmentioning

confidence: 99%

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Venkatesh

Cao

Vaidya

et al. 2016

Meas. Sci. Technol.

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“…To detect the damage, the monitoring technique [71] for detection of early damage in the blade was investigated by using PVDF film sensor and strain gages. Machine learning algorithms based on Artificial Neural Networks (ANNs) [72] was used to address the effectiveness of such methods by combining vibration response data with novelty detection techniques. A new fault prognosis procedure a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (ANFIS) [73] was used to achieve automated detection of significant pitch faults.…”

Section: Simulation For Riskmentioning

confidence: 99%

System safety analysis of large wind turbines

Jin

Zhang

et al. 2016

Renewable and Sustainable Energy Reviews

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“…Various metrics can be considered for measuring the prediction accuracy of the model. The MSE is used for selecting the AANN structure [24,25]. Increasing the number of nodes in the bottleneck layer both improves the network performance (MSE decreases) and increases the FEV [26].…”

Section: Training Aannmentioning

confidence: 99%

Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection

2015

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This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k-means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T 2 multivariate quality control charts are constructed for monitoring the turbine's performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored.

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On damage diagnosis for a wind turbine blade using pattern recognition

Cited by 133 publications

References 22 publications

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

System safety analysis of large wind turbines

Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection

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