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1. Bi-Level Interturn Short-Circuit Fault Monitoring for Wind Turbine Generators With Benchmark Dataset Development

   https://doi.org/10.1115/1.4067056  

   Yan, Jingyi; Senemmar, Soroush; Zhang, Jie (April 2025, Journal of Mechanical Design)

   Abstract Flourished wind energy market pushes the latest wind turbines (WTs) to further and harsher inland and offshore environment. Increased operation and maintenance cost calls for more reliable and cost effective condition monitoring systems. In this article, a bi-level condition monitoring framework for interturn short-circuit faults (ITSCFs) in WT generators is proposed. A benchmark dataset, consisting of 75 ITSCF scenarios and generator current signals of a specific WT, has been created and made publicly available on Zenodo. The data are simulated at a rate of 4 kHz. Based on the time and frequency features extracted from data processing, machine learning-based severity estimation and faulty phase identification modules can provide valuable diagnostic information for wind farm operators. Specifically, the performance of long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs) are analyzed and compared for severity estimation and faulty phase identification. For test-bed experimental reference, various numbers of scenarios for training the models are analyzed. Numerical experiments demonstrate the computational efficiency and robust denoising capability of the CNN algorithm. The GRU network, however, achieves the highest accuracy. The overall system performance improves significantly, from 87.76% with 16 training scenarios to 99.95% with 52 training scenarios, when tested on a set containing all 76 scenarios from an unforeseen period. 

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2. Condition Monitoring and Fault Diagnosis of Generators in Power Networks

   Yang, Bowen; Li, Fangyu; Ye, Jin; Song, Wenzhan. (August 2019, IEEE Power & Energy Society General Meeting)

   In this paper, a novel hierarchical signal processing methodology is proposed for generator condition monitoring and fault diagnosis based on raw electrical waveform data in power networks, which can often be measured by strategically located waveform sensors. The impact of generator short circuit faults on strategically located electrical waveform sensors in power networks are firstly investigated and validated in Matlab Simulink. Based on the large set of electrical waveform data produced by Matlab Simulink, a hierarchical algorithm is then designed to locate fault site location and monitor the condition of generators in power networks. Finally, the proposed methodology is validated in 14-bus IEEE standard power network under different scenarios (e.g, one generator fault, two-generator-fault, various aging levels, etc). Our results show that we can locate fault site location and monitor the aging condition of generators in power networks. Compared to traditional condition monitoring and fault diagnosis based on generator sensors, our proposed methodology can monitor a large number of generators based on a limited number of waveform sensors, which promises to reduce the cost of the maintenance and improve the reliability of the power grid. 

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3. Multivariate Time-Series Modelling for Wind Turbine Subsystem Reliability Prediction

   https://doi.org/10.1115/GT2025-152034  

   Chen, Fuhao; Gao, Linyue (June 2025, American Society of Mechanical Engineers)

   Abstract Wind turbine reliability monitoring and prediction are crucial for early failure detection, proactive maintenance, performance optimization, and cost reduction, especially as many utility-scale turbines near the midpoint or end of their operational lifespan. We proposed a wind turbine subsystem reliability prediction model to facilitate week-ahead forecasts of the occurrence, duration, and type of potential failures or probability of downtime for major turbine components, including the blade, hub, gearbox, generator, inverter, electrical, and control subsystems. Specifically, we developed an Instance-Normalization Decomposition Linear (IN-DLinear) model, grounded in deep time-series modelling theory. The distribution shifts in turbine state features across the training, validation, and test datasets, as well as across various time scales, were effectively mitigated with IN. The long-term inertia in turbine state features was addressed by decomposing the input time-series data to effectively capture seasonality. The efficacy of IN-DLinear is systematically evaluated using 10-year field measurements from a 2.5-MW wind turbine. IN-DLinear exhibited superior performance, reducing prediction errors by 13%∼30% compared to mean value judgment and other deep time-series models, including Seq2Seq, Transformer, and Autoformer. 

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4. An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

   https://doi.org/10.1177/1475921719895588  

   Solimine, Jaclyn; Niezrecki, Christopher; Inalpolat, Murat (January 2020, Structural Health Monitoring)

   This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options. 

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5. Wavelet Based Damage Assessment and Localization for Bridge Structures

   Tan, Chengjun; Elhattab, Ahmed; Uddin, Nasim. (March 2017, Chengjun Tan, Ahmed Elhattab, Nasim Uddin)

   Bridges as a key component of road networks require periodic monitoring to detect structural degradation for early warning. In term of maintaining the bridge safety, it is essential to estimate the damage location and extent. This paper hypothetically investigates employing the wavelet transform to analysis the signal of a vehicle/bridge system to localize and estimate the damage severity. The paper investigated the feasibility of using direct measurements from the bridge system, in compare with using indirect measurements from a crossing inspection vehicle. The study utilizes an implicit Vehicle-Bridge Interaction (VBI) algorithm to simulate the passage of the instrumented vehicle over the bridge to generate the signal; then the signals are processed using Wavelet Transform. The study found that using the indirect vehicle measurements is more sensitive to bridge damage since the vehicle acts as a moving sensor over the bridge. Further, the paper shows promising results for damage detection using the bridge displacement responses, if the static component of the displacement is removed from the recorded displacement history. 

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